Hybrid satellite networks, composed of Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) systems, are capable of ensuring seamless and flexible data exchange across entities. However, the inherent heterogeneity presents critical challenges for cross-domain data aggregation. Specifically, the following issues remain unsolved for current cross-domain data aggregation designs, including insufficient adaptability to the dynamic hierarchical network topologies, inflexible leader election for intra-domain data aggregation, and unsound privacy preservation for inter-domain data transmission. To overcome these limitations, a cross-domain heterogeneous data aggregation scheme for hybrid satellite networks is developed, providing dynamic group key agreement. First, an efficient re-authentication mechanism is constructed to ensure de-synchronization resistance. Meanwhile, a flexible and adaptive leader election strategy is proposed to enhance stable and seamless data exchange among dynamic LEO networks. Additionally, a secure dynamic cross-domain data transmission method is designed to resist eavesdropping and replay attacks. The security proofs and discussions regarding vital security properties are presented, while the performance analysis follows. Compared with the state-of-the-art, advantages in terms of security and performance properties can be proved.

In recent years, numerous group authentication and key agreement (GAKA) schemes have been proposed for the Industrial Internet of Things (IIoTs). However, the frequent identity updates that are a feature of IIoT environments mean that existing schemes cannot maintain full-lifecycle device anonymity. Meanwhile, static integrity verification approaches cannot effectively cope with the dynamic and heterogeneous nature of industrial networks, making it imperative to design an adaptive data integrity protection mechanism to maintain reliable and resilient communication. Additionally, the simultaneous access of a large number of intelligent industrial devices to gateway nodes creates significant computational and communication challenges, while also increasing the risk of denial-of-service attacks and other concurrent threats. Consequently, current schemes are unable to strike an optimal balance between efficiency and security in large-scale IIoT deployments. In our scheme, we first designed an anonymous token mechanism to achieve sufficient randomness for unlinkability when communicating with the same gateway node across different time periods. Secondly, integrating associated data into the authenticated sponge construction (ASCON) encryption process ensures the legitimacy and integrity of the data during transmission. Third, we conduct rigorous security proofs under the widely accepted Algebraic Group Model (AGM) and Random Oracle Model (ROM), demonstrating that our group authentication mechanism is unforgeable against adaptive chosen-public-key and adaptive chosen-subspace attacks. Finally, the performance evaluation demonstrates that our proposed scheme achieves improvement in computational efficiency, while the communication overhead analysis confirms that the design remains both efficient.

As Flying Ad-hoc Network (FANET) evolves toward larger scales and higher levels of autonomy, the importance of secure and efficient group communication continues to grow. However, resource-constrained unmanned aerial vehicles (UAVs) face dual challenges: limited computational power struggles to meet the high demands of complex cryptographic algorithms, while bandwidth constraints exacerbate communication overhead caused by multi-round interaction mechanisms. Moreover, existing solutions find it hard to support dynamic group environments and are prone to single point of failure (SPoF) in centralized architectures, which significantly compromises system reliability and scalability. To address these issues, this paper proposes a novel key agreement protocol for FANET. The protocol employs an improved tree-based key encapsulation mechanism (iTreeKEM) to support rapid key updates in highly dynamic environments. It reduces the computational cost for each group member by 90.08% even when the group size reaches 128. To further enhance system robustness, the protocol introduces a smart contract-based distributed leader election mechanism, effectively eliminating SPoF. The security of the proposed protocol is guaranteed by the CDH problem under the generalized selective decryption (GSD) model. Finally, we implement the protocol in NS-3 simulations, and the results demonstrate its effective applicability to FANET.

The rapid expansion of IoT and edge networks has increased vulnerability to security threats, notably Distributed Denial-of-Service (DDoS) attacks, which target the application layer and can overwhelm networks. This paper introduces a novel two-stage DDoS detection pipeline for IoT and edge networks that utilizes large language model (LLM) embeddings for textual protocol fields along with numeric features like TCP handshake metrics. The first stage accurately identifies traffic as Normal or DDoS, while the second stage classifies confirmed DDoS flows into specific types (UDP, ICMP, TCP, HTTP), correcting any initial false positives. Comprehensive testing on a specialized IoT dataset demonstrated perfect detection rates of 100% in the first stage and 99.91% in subclass classification, highlighting the effectiveness of combining LLM-based textual data with traditional numeric indicators for advanced intrusion detection.

With the rise of blockchain and next-generation networks (NGNs) technologies, privacy and security issues have become the main bottlenecks of the conventional network architecture solution. The asymmetric group key agreement (AGKA) protocol is a practical and effective cryptographic primitive in achieving decentralized authentication for zero-trust architecture. However, conventional AGKA protocols suffer from collusion attack issues and cannot provide privacy-preserving authentication with traceability. This paper aims to develop novel approaches for privacy-preserving authentication and secure data sharing for zero-trust architecture. First, we construct an AGKA protocol, which is so far the first AGKA protocol immune to the collusion attack. Next, we develop an anonymous authentication (AA) scheme that supports the anonymity and traceability properties simultaneously. Moreover, benefiting from the immutability of blockchain, the identity of malicious users can be detected. Third, we design a conditional data sharing (CDS) scheme by means of the secret sharing scheme, which could guarantee the confidentiality and reliability of the users' data from multi-cloud. Finally, we conduct security and performance evaluations, which show that the developed schemes can resist different kinds of attacks with high performance.

Distributed Denial of Service (DDoS) attacks remain a persistent and formidable threat, often overwhelming targets by saturating network bandwidth or exhausting server resources with massive volumes of malicious traffic. Traditional detection methods typically rely on signature-based approaches or large labeled training sets, posing challenges in rapidly changing attack landscapes where novel or variant DDoS vectors emerge frequently. To address this gap, we propose ProtoMAML, a hybrid meta-learning framework that integrates Prototypical Networks and Model-Agnostic Meta-Learning (MAML) to facilitate robust few-shot DDoS detection. By combining prototype-based clustering with fast, gradient-driven adaptation, ProtoMAML can accurately detect new attacks from only a handful of labeled flows per class. Experiments on a large-scale flow dataset (nearly half a million flows) demonstrate that ProtoMAML achieves a recall of up to 99.4% under severe data scarcity. Extended discussions on computational overhead, adversarial resilience, and real-world deployment provide insights into how meta-learning can offer a powerful, agile defense against evolving cyber threats.

Cricket is one of the most popular open-air and unpredictable sports, producing a large volume of quantifiable data. As the popularity of hundred-ball cricket (HBC) games grows, the most difficult challenge is predicting the results of cricket matches, where the high precision score prediction is still anticipated. Traditional methodologies and algorithms for forecasting the probable outcome of a match are restricted in terms of dependability and timeliness, particularly in the case of HBC. In this paper, we study cricket game events and related big datasets and propose CricWin, a web-based approach to predict the outcome of cricket matches for both innings. To predict a match winner, we use a deep learning approach that has yet to be utilized in cricket, particularly in HBC. We propose a Multi-Layered Deep Learning Perceptron (MLDLP) network-based classification model in the deep learning approach to construct different models for predicting the outcome of both innings of a match. We construct a web application to do predictive analysis on a live HBC tournament to predict the game's outcome before the game begins and/or on ongoing matches. The evaluation result demonstrates that CricWin can predict outcomes with up to 100% accuracy, showing that the proposed MLP strategy is dependable and perfect.

The rapid evolution of Intelligent Transportation Systems (ITS) in the Big Data era, propelled by the Internet of Things (IoT), has led to advanced data-driven vehicle traffic forecasting. Graph Neural Networks (GNNs), particularly the Attention-Based Spatial-Temporal Graph Neural Networks (AST-GNN), are promising in traffic forecasting but face limitations in integrating Big Data with privacy-preserving Federated Learning (FL) due to unique data topology processing. This paper intro-duces the Availability Aware Federated Attention-based Spatial-Temporal Graph Neural Network (FastFlow), an innovative framework that enhances ASTGNN by integrating Federated Learning across entities and employing Big Data methodologies. FastFlow's distinctiveness lies in its availability-aware approach, aggregating adjacency matrices for global topology and utilizing a novel communication protocol that prioritizes data availability and correlation among organizations. Our evaluation of Caltrans Performance Measurement System (PEMS) data in a simulated setup demonstrates FastFlow's ability to balance predictive accuracy and data security in a multi-organizational context.

Federated learning of deep neural networks has emerged as an evolving paradigm for distributed machine learning, gaining widespread attention due to its ability to update parameters without collecting raw data from users, especially in digital healthcare applications. However, the traditional centralized architecture of federated learning suffers from several problems (e.g., single point of failure, communication bottlenecks, etc.), especially malicious servers inferring gradients and causing gradient leakage. To tackle the above issues, we propose a robust and privacy-preserving decentralized deep federated learning (RPDFL) training scheme. Specifically, we design a novel ring FL structure and a Ring-Allreduce-based data sharing scheme to improve the communication efficiency in RPDFL training. Furthermore, we improve the process of distributing parameters of the Chinese residual theorem to update the execution process of the threshold secret sharing, supporting healthcare edge to drop out during the training process without causing data leakage, and ensuring the robustness of the RPDFL training under the Ring-Allreduce-based data sharing scheme. Security analysis indicates that RPDFL is provable secure. Experiment results show that RPDFL is significantly superior to standard FL methods in terms of model accuracy and convergence, and is suitable for digital healthcare applications.

Summary Nowadays, wireless radio signals are ubiquitous and are around us; some signals pass through us, and some reflect off us. Substantial advancements in recent years demonstrate that such signals are utilized for diverse emerging applications, including people activity, motion watches, healthcare, and so forth. A few questions would be that may raise severe concerns in future cybersecurity and private domains. For example, what if Wi‐Fi signals are utilized to watch a person doings and actions, which are mostly without the person's authorization and authentication. How far such signal utilization can attack privacy intrusively, silently, more particularly, what/where we do, say, command, see, write, draw, go, perform, everything can be known. In this article, we investigate watching human activities by leveraging Wi‐Fi signals and discuss a few application prototypes. We attempt to learn whether or not attackers have the ability to passively watch our Internet activity as well as physical activities and motions through Wi‐Fi. With all‐new advances, one must be aware that cyberattackers may apply unauthorized use of these advances to their benefit. We discuss some of the countermeasures and approaches to mitigate these risks with Wi‐Fi signal leveraging.

The papers in this special section focus on the trustworthiness of artificial intelligence/machine learning models/deep learning models (AI/ML/DL) as it applies to the Industrial Internet of Things (IIot). This includes automated environments, such as smart factories, smart airports, and smart healthcare systems. AI approaches enable automation and data analytic across industrial technologies, including the IIoT, cloud and edge, and fog computing paradigms. Current ML models, such as DL still suffer from designing a generalized trustworthy architecture that reveals semantics and contexts of models and attacks threat surface. The papers in this section were inspired by the convincing challenges and necessities described above and attempt to compile research results that essentially adopts them.

The 5G-enabled Industrial Internet of Things tilts the data processing model from the cloud to the edge. Users are more inclined to get feedback and data analysis of outsourcing computing results timely from the edge. However, the existing solutions undermine the fairness of multitask outsourcing in edge environment and cannot guarantee the correctness of results. To tackle these challenges, in this article, we propose a fair, verifiable, and privacy-preserving edge outsourcing computing scheme based on blockchain (FVP-EOC). Initially, we propose a task bidding method in the same round of task outsourcing, which improves the utilization of resources and the fairness of the FVP-EOC by dividing tasks into blocks. Furthermore, we design a result verification algorithm and a consensus algorithm to ensure the correctness of the results without a trusted third party. Finally, theoretical analysis and ample simulations indicate that the FVP-EOC is secure and verifiable and ensure the benefits of all the participants in edge outsourcing computing.

Recently, artificial intelligence approaches are widely suggested to optimize numerous offloading task-scheduling purposes. However, they confront difficulties in maintaining data privacy regarding the context of the data offloading during the course of offloading in the different stages. To address this problem, in this article we proposeC-fDRL, a framework to provide context-aware federated deep reinforcement learning (fDRL) to maintain the context-aware privacy of the task offloading. We perform this in three stages (CloudAI, EdgeAI, and DeviceAI) of the overall system.C-fDRLchecks whether the privacy of high-context-aware data with the task being offloaded is maintained locally at the DeviceAI, and low-context-aware data distributedly at the EdgeAI. When there is an offloading task request or a user needs to offload the data,C-fDRLuses a context-aware data management approach to decouple the context-aware (privacy) data from the tasks. This separates the context-aware data from the task for local computation and allows a new scheduling technique called “context-aware multilevel scheduler.” This places high-context-aware data on local devices and low-context-aware data at the edge device for computation before the actual task execution. We performed experiments to evaluate the data privacy with the offloading tasks and the federated DRL. The results show that the proposedC-fDRLperforms better than the existing framework.

Various attacks in communication network threaten the security of vehicular system. For cooperative location-sensing system, man-in-the-middle attack and eavesdropping attack would lead to catastrophic collapse of vehicular network localization. In this paper, we propose a federated cryptosystem localization based on optimized constraints. In the localization phase, a message passing algorithm combining belief propagation (BP) and variational message passing (VMP) is derived by defining penalty function, which can detect the distance outliers. The messages generated by each vehicular node or base station are encrypted with Paillier cryptosystem. Because Paillier cryptosystem is featured by homomorphic addition, message aggregation reduces large amount of decryption. The localization messages finally work in a federated transmission scheme for privacy preserving against man-in-the-middle attack and eavesdropping attack. In terms of localization accuracy, algorithm parameters, convergence analysis and efficiency, simulation results show that cybersecurity of cooperative localization is proved to be effective in various scenarios of vehicular network.

Elections make a fundamental contribution to democratic governance and are very important to select the appropriate person to determine the fate of a nation while a large number of citizens do not trust the ballot-paper-based voting system. As a result, e-Voting is being adopted by various countries throughout the world. With advantages, there are a number of constraints of e-voting systems, a single vulnerability can lead to large-scale manipulations of voting results. Leveraging and intersecting both cutting-edge technologies including biometric and blockchain technology can address the limitations of current e-Voting frameworks. In this research, we analyze the requirements for systems modeling of voting technology and possible opportunities to adopt biometrics and blockchain technology. Based on the requirements analysis, we propose a biometric-enabled and hyperledger fabric-based voting framework to automate identity verification that will ensure transparency and security of electronic voting. We utilize the software architecture analysis method and active reviews for intermediate designs for evaluating the proposed framework. Demonstration indicates the efficiency and quality attributes of architectural design that shall lead us to implement the system in real-world scenarios in future studies. Initially, we develop a Webbased prototype to demonstrate the framework. In future studies, we aim to implement on large scale and evaluate the prototype against the requirements and security standard.

Summary In recent times, many location‐based service providers (LBSPs) choose to outsource data query services to third‐party cloud service providers (CSPs). This allows users to easily search for points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in‐vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead.

In this paper, we deal with the problem of similarity search about crowdedness for participatory-sensing buses for urban transportation. Similarity search is usually applied for measuring similarities in heterogeneous information networks. However, many models implement similarity search in a global setting, without taking object attributes into consideration. OCP, a novel OLAP-based crowdedness perception, is an attribute-enriched and meta-path-based model with machine learning to capture similarity based on the object connectivity, visibility and features. A set of common crowdedness attribute dimensions are defined across different types of objects, which can be obtained from the participatory passenger’s sensor data through deep-neural-network-based posture recognition. Accordingly, an object can be described as a series of node vectors from different dimensions. In such framework, OLAP is applied in analysing multiple resolutions and improving efficiency of similarity search. In addition, our data sources are based on participatory-sensing instead of using vehicle GPS systems. As more data be collected through participatory-sensing, more accurate crowdedness for a bus can be estimated. The experiment results further demonstrate the efficiency of our analytical approaches.

Recommender systems can help correlate information and recommend personalised services to users as a general information filtering tool. However, contextual factors significantly affect user behaviour, especially in the Internet of Things (IoT), which brings difficulties to modelling user preferences. In this paper, we propose a personalised context-aware re-ranking algorithm (p-CAR) in IoT. Our primary purpose is to improve the recommender performance from multiple metrics, such as precision, recall, diversity, and popularity. The core idea is to re-rank the ranking list using the user's preference behaviour under different contexts. The re-ranking process is an iterative selection process; each time an optimal item that meets the target criteria is selected from the candidate items and added to the re-ranked list. The selection of items depends on the given context and the user's interest in that context. User's preference and interest in contexts are both expressed by probability in our algorithm. In addition, we use a weight parameter to control the influence of contexts and model the contextual personalisation of different users through local personalisation parameters. We verify our algorithm through experiments on the real Movielens 100K dataset and show the performance advantage with the existing algorithm.

Internet of Things (IoT) is one of the rapidly developing technologies today that attract huge real-world applications. However, the reality is that IoT is easily vulnerable to numerous types of cyberattacks and anomalies. Detecting them is becoming increasingly challenging day by day due to limitations with IoT devices and threat intelligence. Particularly, one of the most challenging problems is to detect the existence of malicious adversaries that continuously adapt or conceal their behaviors in IoT to hide their actions and to make the IoT security protocol ineffective. In this article, we study this problem at the IoT device level that can be a great idea to avoid potential attacks. We presentAntiConcealer, an edge-aided IoT framework, and propose an edge artificial intelligence-enabled approach (EdgeAI) for detecting adversary concealed behaviors in the IoT. We first develop an adversary behavior model and use this to identify mid-attack temporal patterns by learning the multivariate Hawkes process (MHP), a kind of point process as a random and finite series of events (e.g., behaviors) controlled by a probabilistic model. Naturally, learning MHP processed on EdgeAI reveals the influence of the concealed behaviors of adversaries in the IoT. These concealed behaviors are then grouped using a nonnegative weighted influence matrix. To observe the performance of theAntiConcealerframework through evaluation, we employ honeypots integrated with edge servers and verify the usability and reliability of adversary behavioral identification.

The development of Internet of Vehicles (IoV) has produced a considerable amount of real-time traffic data. These traffic data constitute a kind of digital twin that connects the physical vehicles and their virtual representation via 5G communications. Generally, through analyzing the digital twin traffic data, traffic administrators can optimize traffic scheduling and alleviate traffic jams. However, the exceptions of IoV sensors inevitably raise an issue of traffic data sparsity and consequently influence scientific traffic scheduling decisions. Inspired by this drawback, in this article, a digital twin-assisted real-time traffic data prediction method is proposed by analyzing the traffic flow and velocity data monitored by IoV sensors and transmitted through 5G. At last, we conduct a set of experiments based on a traffic dataset collected by Nanjing city of China. Reported results show the feasibility of our proposal in smart traffic flow and velocity prediction that call for a quick response and high accuracy.

With the advancements of the Health 2.0 technology, large-scale healthcare services are available online. Recommender systems for healthcare services have emerged for decision assistance. Most existing collaborative recommendation algorithms only mine global interactions while failing to capture the local different information of users or items. Besides, privacy concern is another significant problem to be considered in recommender systems for healthcare services. In this article, a privacy-aware factorization-based hybrid method is proposed for healthcare service recommendations. For better modeling of user preferences and service features, multiview embeddings of users and healthcare services are learned. Besides, we address the privacy problem by integrating local differential privacy and locality-sensitive hashing techniques into the recommendation model for privacy-aware neighbor searching. The final prediction is made based on a stochastic gradient descent learning-based hybrid collaborative model. Experiments demonstrate the effectiveness of the proposed method in both recommendation performance and privacy concerns.

Edge services provide an effective and superior means of real-time transmissions and rapid processing of information in the Industrial Internet of Things (IIoT). However, the continuous increase of the number of smart devices results in privacy leakage and insufficient model accuracy of edge services. To tackle these challenges, in this article, we propose a blockchain-based machine learning framework for edge services (BML-ES) in IIoT. Specifically, we construct novel smart contracts to encourage multiparty participation of edge services to improve the efficiency of data processing. Moreover, we propose an aggregation strategy to verify and aggregate model parameters to ensure the accuracy of decision tree models. Finally, based on the SM2 public key cryptosystem, we protect data security and prevent data privacy leakage in edge services. Theoretical analysis and simulation experiments indicate that the BML-ES framework is secure, effective, and efficient, and is better suitable to improve the accuracy of edge services in IIoT.

Depression is one of the most common mental illnesses, and the symptoms shown by patients are different, making it difficult to diagnose in the process of clinical practice and pathological research. Although researchers hope that artificial intelligence can contribute to the diagnosis and treatment of depression, the traditional centralized machine learning methods need to aggregate patient data, and the data privacy of patients with mental illness needs to be strictly confidential, which hinders machine learning algorithms’ clinical application. To solve the problem of medical data privacy with depression, in this article, we implement a study of federated learning to analyze and diagnose depression. First, we propose a general multiview federated learning framework using multisource data, which can extend any traditional machine learning model to support federated learning across different institutions or parties. Second, we employ later fusion methods to solve the problem of inconsistent time series of multiview data. Finally, we compare the federated framework with other cooperative learning frameworks in performance and discuss the related results. The experimental results show that in the case of participating in federated learning with enough participants, the prediction accuracy of depression score can reach 85.13%, which is about 15% higher than local training. When the number of participants is small and the amount of data is sufficient, the prediction accuracy of depression score can also reach 84.32%, and the improvement rate is about 9%.

Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this article, we proposeDRLTrack, a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient network performance. InDRLTrack, a huge number of IoT devices are employed to collect data about a target of interest. One or two edge devices in the network coordinate with a group of IoT devices and collaboratively detect the target by using the C-DRL approach and form an area around the target by the group of IoT devices. To maintain such an area during the tracking time, we employ a deep Q-network to track the target from one group to another. An EdgeAI sitting on the top of the edge devices has the control of the C-DRL approach during tracking and can identify a sequence of tracks.DRLTrackis said to betrustworthyas it shows trustworthy performance in terms of QoT, dynamic environments, and even under certain cyberattacks. We validate the performance ofDRLTrackconsidering the objectives through simulations and it demonstrates superior performance compared with existing work.

Through the collaboration of cloud and edge, cloud-edge computing allows the edge that approximates end-users undertakes those non-computationally intensive service processing of the cloud, reducing the communication overhead and satisfying the low latency requirement of Internet of Vehicle (IoV). With cloud-edge computing, the computing tasks in IoV is able to be delivered to the edge servers (ESs) instead of the cloud and rely on the deployed services of ESs for a series of processing. Due to the storage and computing resource limits of ESs, how to dynamically deploy partial services to the edge is still a puzzle. Moreover, the decision of service deployment often requires the transmission of local service requests from ESs to the cloud, which increases the risk of privacy leakage. In this article, a method for privacy-aware IoV service deployment with federated learning in cloud-edge computing, named PSDF, is proposed. Technically, federated learning secures the distributed training of deployment decision network on each ES by the exchange and aggregation of model weights, avoiding the original data transmission. Meanwhile, homomorphic encryption is adopted for the uploaded weights before the model aggregation on the cloud. Besides, a service deployment scheme based on deep deterministic policy gradient is proposed. Eventually, the performance of PSDF is evaluated by massive experiments.

The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users' daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this article, we consider the vehicular computation offloading problem in mobile-edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multiagent deep reinforcement learning (DRL)-based computation offloading scheme, in which the uncertainty of a multivehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. First, we formalize a formula for the computation offloading problem. The goal of this article is to determine the optimal offloading decision to the MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multiagent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.

Secret sharing scheme has been applied commonly in distributed storage for Big Data. It is a method for protecting outsourced data against data leakage and for securing key management systems. The secret is distributed among a group of participants where each participant holds a share of the secret. The secret can be only reconstructed when a sufficient number of shares are reconstituted. Although many secret sharing schemes have been proposed, they are still inefficient in terms of share size, communication cost and storage cost; and also lack robustness in terms of exact-share repair. In this paper, for the first time, we propose a new secret sharing scheme based on Slepian-Wolf coding. Our scheme can achieve an optimal share size utilizing the simple binning idea of the coding. It also enhances the exact-share repair feature whereby the shares remain consistent even if they are corrupted. We show, through experiments, how our scheme can significantly reduce the communication and storage costs while still being able to support direct share repair leveraging lightweight exclusive-OR (XOR) operation for fast computation.

The mobile crowdsensing (MCS) technology with a large number of Internet of Things (IoT) devices provides an economic and efficient solution to participation in coordinated large-scale sensing tasks. Edge computing powers MCS to form the mobile edge crowdsensing (MECS) framework. Privacy disclosure of sensing data in multiple stages is a significant challenge in the MECS. To tackle this issue, combining machine learning with game theory, in this article, we propose an artificial intelligence (AI)-enabled three-party game (ATG) framework for guaranteed data privacy in the MECS of IoT. Specifically, based on the random forest classifier and the k-anonymity algorithm, we propose a classification-anonymity model that effectively guarantees the privacy of sensitive data. Moreover, we construct a three-party game model for analyzing the data privacy leakage in different phases in the MECS. Finally, we conduct numerical and theoretical analyses and ample simulations. The results indicate that the ATG framework is effective and efficient, and better suited to the MECS of IoT.

Internet of Vehicles (IoV) supports multiple traffic services by processing abundant data from sensors and video surveillance devices. With edge computing, video surveillance services can be certainly improved due to the handy resource provision for video storage and processing. Generally, to reduce the hardware and maintenance investment, it is a popular manner to deploy the limited amount of edge nodes along with the surveillance devices. However such edge node layout leads to the unstable service distribution and complicated data transmission across the surveillance devices and edge nodes, which consequently decreases the quality of the surveillance services. In addition, the service trustworthiness is suspected since the privacy information may be revealed to some extent during the data transmission. To combat these challenges, a trust-aware task offloading method (TOM) for video surveillance in edge computing enabled IoV is presented for minimizing the response time of the services, achieving the load balance of the edge nodes and realizing privacy protection. Technically, SPEA2 (improving the strength Pareto evolutionary algorithm) is employed to acquire balanced task offloading solutions. Then, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and MCDM (Multiple Criteria Decision Making) are exercised to ascertain the optimal solution. Finally, the experimental simulation demonstrates that TOM performs efficient and trust.

Currently, Edge computing (EC) paradigm is adopted to provision the low-latency resources for the massive real-time services in Internet of vehicles (IoV). To alleviate the QoE (Quality of Experience) degradation of the vehicular users due to the uncertainties (e.g., resource conflicts and communicating interruption), software-defined network (SDN) is involved in the EC-enabled IoV to manage the cooperative operation of distributed edge nodes (ENs). However, the increasing privacy leakage for the IoV service offloading causes the disclosure of the sensitive information, including driving location, personal information of the driver, etc. Moreover, the regulation of SDN is practically insufficient, as the general control is incompetent to maintain balanced operation with the premise of efficient service utility. In view of these challenges, a secure service offloading method, named SOME, is designed to promote IoV service utility and edge utility, meanwhile ensuring privacy security, in SDN-enabled EC. Specifically, an SDN-based framework for IoV service management is developed to address the inherent uncertainty of edge network by SDN controllers. Besides, the locality-sensitive-hash (LSH) is leveraged to realize utility- and privacy-aware service selection. Eventually, comparative experiments are implemented to verify the effectiveness of SOME.

Nowadays, the applications related to Internet of connected vehicles (IoCV) have been greatly promoted by the roadside units (RSUs). To improve the transmission efficiency by the RSUs, 5G is introduced to the IoCV scenario for offering sufficient communication bandwidth. Generally, the traditional offloading destinations of the computing tasks in IoCV are the distant cloud servers, which consequently increases the response time of the tasks. Edge servers, placed together with macro base stations (MABSs) in 5G and RSUs, offer alternatives to host the tasks. However, the complicated locations of MABSs and RSUs make it difficult to distinguish the offloading destinations of the computing tasks in IoCV. In view of this, an adaptive computation offloading method, named ACOM, is devised for edge computing in 5G-envisioned IoCV to optimize the task offloading delay and resource utilization of the edge system. More specifically, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is fully leveraged to generate the available solutions. Then, the optimal offloading solution is obtained by utility evaluation. Eventually, the experimental results demonstrate the effectiveness of ACOM.

Mobile crowdsensing is an emerging paradigm that selects users to complete sensing tasks. Recently, mobile vehicles are adopted to perform sensing data collection tasks in the urban city due to their ubiquity and mobility. In this article, we study how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes. We formulate the recruitment of vehicles as a maximum data limited budget problem. The application scenario is generalized to a realistic online setting where vehicles are continuously moving in real-time and the data center decides to recruit a set of vehicles immediately. A deep learning-based scheme through mobile vehicles (DLMV) is proposed to collect sensing data in the urban environment. We first propose a deep learning-based offline algorithm to predict vehicle mobility in a future time period. Furthermore, we propose a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem. Extensive experimental evaluations are conducted on the real mobility dataset in Rome. The results have not only verified the efficiency of our proposed solution but also validated that DLMV can improve the quantity of collected sensing data compared with other algorithms.

Using unsupervised learning methods for clinical diagnosis is very meaningful. In this study, we propose an unsupervised multi-view & multi-medoid variant-entropy-based fuzzy clustering (M 2 VEFC) method for epilepsy EEG signals detecting. Comparing with existing related studies, M 2 VEFC has four main merits and contributions: (1) Features in original EEG data are represented from different perspectives that can provide more pattern information for epilepsy signals detecting. (2) During multi-view modeling, multi-medoids are used to capture the structure of clusters in each view. Furthermore, we assume that the medoids in a cluster observed from different views should keep invariant, which is taken as one of the collaborative learning mechanisms in this study. (3) A variant entropy is designed as another collaborative learning mechanism in which view weight learning is controlled by a user-free parameter. The parameter is derived from the distribution of samples in each view such that the learned weights have more discrimination. (4) M 2 VEFC does not need original data as its input—it only needs a similarity matrix and feature statistical information. Therefore, the original data are not exposed to users and hence the privacy is protected. We use several different kinds of feature extraction techniques to extract several groups of features as multi-view data from original EEG data to test the proposed method M 2 VEFC. Experimental results indicate M 2 VEFC achieves a promising performance that is better than benchmarking models.

Modern societies are becoming increasingly reliance on inter-connected digital systems. Despite numerous benefits, it is important to overcome existing security problems in a highly inter-connected system, like Sensor-Cloud systems. Sensor-Cloud is the product of the integration of wireless sensor networks and cloud computing. However, when a physical sensor node receives multiple service commands simultaneously, there will be some service collisions, namely, coupling security problem. This coupling security problem may lead to the failure of sustainable services and the system security threat. In order to solve the problem, sustainable resource management and maximum resource utilization are important. In this paper, we extend the Kuhn-Munkres algorithm based on fog computing to achieve sustainability. To begin with, we design a buffer queue in fog computing layer which will return the result to the cloud layer directly to increase the resource utilization. Then, we extend the Kuhn-Munkres algorithm to get the initial assignments of resources. The last step is to determine whether the initial assigned resources can be further scheduled, which means that we further improve the resource utilization to realize sustainable resource management. The results demonstrate that our method outperforms the traditional scheduling methods, which decreases both of the rounds and computational costs of scheduling by 24.04-57.78 percent and 9.88-31.51 percent, respectively. The experimental evaluations proved that the performance of the proposed fog-based scheme can effectively solve coupling security problem for sustainable Sensor-Cloud systems.

Network anomalies are correlated to activities that deviate from regular behavior patterns in a network, and they are undetectable until their actions are defined as malicious. Current work in network anomaly detection includes network-based and host-based intrusion detection systems. However, most of them suffer from high false detection rates due to the base rate fallacy. To overcome such a drawback, this paper proposes a superior behavior-based anomaly detection system (SuperB) that defines legitimate network behaviors of authorized users in order to identify unauthorized accesses. We define the network behaviors of the authorized users by training the proposed deep learning model with time-series data extracted from network packets of each of the users. Then, the trained model is used to classify all other behaviors (we define these as anomalies) from the defined legitimate behaviors. As a result, SuperB effectively detects all anomalies of network behaviors. Our simulation results show that the proposed algorithm needs at least five end-to-end conversations to achieve over 95% accuracy and over 93% recall rate. Some simulations show 100% accuracy and recall rate. Our simulations use live network data combined with the CICIDS2017 data set. The performance has an average of less than 1.1% false-positive rate with some simulations showing 0%. The execution time to process each conversation is 85.20±0.60 milliseconds (ms), and thus it takes about only 426 ms to process five conversations to identify anomaly.

In smart cities, data privacy and security issues of Electronic Health Record(EHR) are grabbing importance day by day as cyber attackers have identified the weaknesses of EHR platforms. Besides, health insurance companies interacting with the EHRs play a vital role in covering the whole or a part of the financial risks of a patient. Insurance companies have specific policies for which patients have to pay them. Sometimes the insurance policies can be altered by fraudulent entities. Another problem that patients face in smart cities is when they interact with a health organization, insurance company, or others, they have to prove their identity to each of the organizations/companies separately. Health organizations or insurance companies have to ensure they know with whom they are interacting. To build a platform where a patient's personal information and insurance policy are handled securely, we introduce an application of blockchain to solve the above-mentioned issues. In this paper, we present a solution for the healthcare system that will provide patient privacy and transparency towards the insurance policies incorporating blockchain. Privacy of the patient information will be provided using cryptographic tools.

Blockchain technology has evolved through many changes and modifications, such as smart-contracts since its inception in 2008. The popularity of a blockchain system is due to the fact that it offers a significant security advantage over other traditional systems. However, there have been many attacks in various blockchain systems, exploiting different vulnerabilities and bugs, which caused a significant financial loss. Therefore, it is essential to understand how these attacks in blockchain occur, which vulnerabilities they exploit, and what threats they expose. Another concerning issue in this domain is the recent advancement in the quantum computing field, which imposes a significant threat to the security aspects of many existing secure systems, including blockchain, as they would invalidate many widely-used cryptographic algorithms. Thus, it is important to examine how quantum computing will affect these or other new attacks in the future. In this paper, we explore different vulnerabilities in current blockchain systems and analyse the threats that various theoretical and practical attacks in the blockchain expose. We then model those attacks using Petri nets concerning current systems and future quantum computers.

Mobile Health (mhealth) applications are widely used applications to monitor our health, well being and daily activities. The privacy and security of personal information while using mHealth apps are of great concerns. In this paper, we propose a method based on HIPAA privacy rule and security rule to analyze mHealth applications. There are total four parts in this method: privacy policy analysis, static analysis, dynamic analysis, and HTTP analysis. Through our analysis, we identify that mHealth applications are not as safe as they are shown in the posted privacy policy. Many mhealth apps could cause damage to personal privacy.

With the emerging concept of Wi-Fi radio as sensors, we are witnessing more device-free sensing applications. But we observe that most of the existing works of these applications are meant for simple indoor layout and are not adequate for complex cases, e.g., collapsed structures. In this article, we explore the feasibility of Wi-Fi Halow signals for the collapsed scenario as it can boost rescue efforts. To achieve this, we aim at two prime objectives of this article. First, we model debris constituent of common collapsed scenario materials, such as concrete, brick, glass, and lumber by conducting a field survey of an earthquake-affected area. After that, we consider signal propagation models for better coverage in this debris model by employing two methods. The first method is an integrated TOPSIS and Shannon entropy-based on a bijective soft set, which provides us an approximation tool to select the best Wi-Fi Halow signal coverage in debris. The second method composes two modified wireless signal propagation models, which are transmitter-receiver (TR) and Wi-Fi radar, respectively. We perform extensive simulations and figure out that low power transmission using Wi-Fi radar can yield better coverage, which is also verified by the Shannon entropy method.

For the cloud computing based on software-defined networks (SDNs), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer service response time due to the capacity-limited Internet. To solve this problem, a novel service orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and service response delay. In SODA, the network is divided into three layers. 1) Data centers layer (DCL). Data centers (DCs) release software with a specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce service response delay. 2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along the same routing path is higher to reduce redundancy data. 3) Vehicle network layer (VNL). Mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation is conducted. The results illustrate that the proposed scheme has better performance compared with the traditional scheme.

The Internet of Things (IoT) offers new opportunities for cities to make citizens live and work in more sustainable, healthy, and safe places. Since IoT applications in smart cities are characterized by different devices, networking standards, and data management strategies, trust becomes a fundamental issue in the IoT ecosystem. The explosion of IoT devices, along with their decentralized deployment, constraint resources, limited computational and cryptographic capabilities, brings challenges to trust management in IoT. The coexistence of multiple IoT domains also raises challenges, for example, how to evaluate and maintain trust across domain boundaries. This special issue aims at bringing the researchers from both academia and industry together to disseminate their recent advances related to the challenges and solutions in building trustful IoT for smart cities.

Internet of Things (IoT) devices and the edge jointly broaden the IoT's sensing capability and the monitoring scope for various applications. Though accessing sensing data and making decisions through IoT smart devices turns out to be commonplace, it is challenging to guarantee user privacy and preserve the accuracy (integrity) of the collected data. The IoT smart devices frequently lose either IoT user's privacy or data integrity. This also makes it crucial to put a threshold on the cost of computation and load of the IoT devices, as gradually more IoT services demand access to the resources that devices offer. In this article, we propose BalancePIC, a scheme that attempts to preserve a balance in the three aspects (user privacy, data integrity in edge-assisted IoT devices, and the computational cost). It achieves the balance through a balanced truth discovery approach and a proposed enhanced technique for data privacy, which are used in IoT devices and edge server interactions. It authenticates the IoT user participation with privacy in the truth discovery process through a biometric-ECC-based authentication algorithm. The nature of the BalancePIC scheme is to straightforwardly provide the likelihood for a simple amendment on the cryptography technique and weight assignment. This lessens the overall computational cost for the IoT user devices but also restricts the communications between the user devices and the edge server, which is important for data integrity. We present an enhanced technique to preserve privacy by guarding the user from potential threats and suspicious data collection parties. To achieve this, BalancePIC takes steps to blur the original sensory data of the device by processing results in groups called zones. Simulation result analysis provides evidence for the balance preservation in the three aspects.

It is generally accepted that the edge computing paradigm is regarded as capable of satisfying the resource requirements for the emerging mobile applications such as the Internet of Things (IoT) ones. Undoubtedly, the data collected by underlying sensor networks are the foundation of both the IoT systems and IoT applications. However, due to the weakness and vulnerability to attacks of underlying sensor networks, the data collected are usually untrustworthy, which may cause disastrous consequences. In this article, a new model is proposed to collect trustworthy data on the basis of edge computing in the IoT. In this model, the sensor nodes are evaluated from multiple dimensions to obtain accurately quantified trust values. Besides, by mapping the trust value of a node onto a force for the mobile data collector, the best mobility path is generated with high trust. Moreover, a mobile edge data collector is used to visit both the sensors with quantified trust values and collect trustworthy data. The extensive experiment validates that the IoT systems based on trustworthy data collection model gain a significant improvement in their performance, in terms of both system security and energy conservation.

Currently, edge computing (EC), emerging as a burgeoning paradigm, is powerful in handling real-time resource provision for Internet of Things (IoT) applications. However, due to the spatial distribution of geographically sparse IoT devices and the resource limitations of EC units (ECUs), the resource utilization of corresponding edge servers is relatively insufficient and the execution performance is ineffective to some extent. A privacy leakage, including personal information, location, media data, etc., during the transmission process from IoT devices to edge servers severely restricts the application of ECUs in IoT. To address these challenges, a two-phase offloading optimization strategy is put forward for joint optimization of offloading utility and privacy in EC enabled IoT. Technically, a utility-aware task offloading method, named UTO, is devised first to obtain the goal of maximizing the resource utilization of ECUs and minimizing the implementation time cost. Then a joint optimization method, named JOM, for utility and privacy tradeoffs is designed to balance the privacy preservation and execution performance. Eventually, the experimental evaluations are designed to illustrate the efficiency and reliability of UTO and JOM.

Extracting knowledge from sensor data for various purposes has received a great deal of attention by the data mining community. For the purpose of event detection in cyber-physical systems (CPS), e.g., damage in building or aerospace vehicles from the continuous arriving data is challenging due to the detection quality. Traditional data mining schemes are used to reduce data that often use metrics, association rules, and binary values for frequent patterns as indicators for finding interesting knowledge about an event. However, these may not be directly applicable to the network due to certain constraints (communication, computation, bandwidth). We discover that, the indicators may not reveal meaningful information for event detection in practice. In this paper, we propose a comprehensive data mining framework for event detection in the CPS named DPminer, which functions in a distributed and parallel manner (data in a partitioned database processed by one or more sensor processors) and is able to extract a pattern of sensors that may have event information with a low communication cost. To achieve this, we introduce a new sensor behavioral pattern mining technique called differential sensor pattern (DSP) which considers different frequencies and values (non-binary) with a set of sensors, instead of traditional binary patterns. We present an algorithm for data preparation and then use a highly-compact data tree structure (called DP-Tree) for generating the DSP. An important tradeoff between the communication and computation costs for the event detection via data mining is made. Evaluation results show that DPminer can be very useful for networked sensing with a superior performance in terms of communication cost and event detection quality compared to existing data mining schemes.

Trustworthiness is the probability that a system will function according to intended behaviors under a set of circumstances as demonstrated by qualities including, but not limited to safety, security, privacy, reliability, real timeliness. Trustworthiness in the industrial Internet of Things (IIoT) systems and applications is crucial to a vital expectation of industrial investors. Preserving the trustworthiness of such a system and network is crucial to void cost, time, and loss of lives. A trustworthy IIoT system considers both the security characteristics and system functionalities (faults, failures) of IoT trustworthiness. Traditional security systems, tools, techniques, and apps are not enough to protect the IIoT platform due to the practical facts in real industrial environments, diverse protocols, constrained upgrade opportunities, mismatch in protocols, and resource constraints in the industrial system. Regarding these concerns, this special section targets to bring up-to-date research results of IIoT systems and applications with the trustworthiness support. This leads to steady operations and high-quality results and improved safety in the IIoT.

Artificial intelligence has achieved great success in the field of medical-assisted diagnosis, and a deep learning technology plays a very important role in medical image recognition. However, it usually takes medical institutions extra time, energy, and cost to obtain a credible and efficient deep learning model, which is not conducive to a wide range of applications, including medical image recognition and medical decision making. In this article, we propose a novel medical-assisted diagnosis model as a service (MDMaaS). Medical institutions can obtain and use the medical-assisted diagnosis models from the service providers directly; a model training and a model application in machine learning are assigned to a service provider and a consumer, respectively. We have designed a model acquisition method based on the conventional samples and small samples for MDMaaS providers, and we have also developed a trustworthy model-based recommendation method for MDMaaS consumers, which would help the medical institutions to obtain the reliable medical-assisted diagnosis models quickly and efficiently. Based on the MDMaaS, extensive experiments are performed to verify the effectiveness of the proposed method.

In this article, an industrial cyber-physical system (ICPS) is utilized for monitoring critical events such as structural equipment conditions in industrial environments. Such a system can easily be a point of attraction for the cyberattackers, in addition to system faults, severe resource constraints (e.g., bandwidth and energy), and environmental problems. This makes data collection in the ICPS untrustworthy, even the data are altered after the data forwarding. Without validating this before data aggregation, detection of an event through the aggregation in the ICPS can be difficult. This article introduces TrustData, a scheme for high-quality data collection for event detection in the ICPS, referred to as “Trust worthy and secured Data collection” scheme. It alleviates authentic data for accumulation at groups of sensor devices in the ICPS. Based on the application requirements, a reduced quantity of data is delivered to an upstream node, say, a cluster head. We consider that these data might have sensitive information, which is vulnerable to being altered before/after transmission. The contribution of this article is threefold. First, we provide the concept of TrustData to verify whether or not the acquired data are trustworthy (unaltered) before transmission, and whether or not the transmitted data are secured (data privacy is preserved) before aggregation. Second, we utilize a general measurement model that helps to verify acquired signal untrustworthy before transmitting toward upstream nodes. Finally, we provide an extensive performance analysis through a real-world dataset, and our results prove the effectiveness of TrustData.

Urban traffic passenger flows prediction is practically important to facilitate many real applications including transportation management and public safety. Recently, deep learning based approaches are proposed to learn the spatio-temporal characteristics of the traffic passenger flows. However, it is still very challenging to handle some complex factors such as hybrid transportation lines, mixed traffic, transfer stations, and some extreme weathers. Considering the multi-channel and irregularity properties of urban traffic passenger flows in different transportation lines, a more efficient and fine-grained deep spatio-temporal feature learning model is necessary. In this paper, we propose a deep irregular convolutional residual LSTM network model called DST-ICRL for urban traffic passenger flows prediction. We first model the passenger flows among different traffic lines in a transportation network into multi-channel matrices analogous to the RGB pixel matrices of an image. Then, we propose a deep learning framework that integrates irregular convolutional residential network and LSTM units to learn the spatial-temporal feature representations. To fully utilize the historical passenger flows, we sample both the short-term and long-term historical traffic data, which can capture the periodicity and trend of the traffic passenger flows. In addition, we also fuse other external factors further to facilitate a real-time prediction. We conduct extensive experiments on different types of traffic passenger flows datasets including subway, taxi and bus flows in Beijing as well as bike flows in New York. The results show that the proposed DST-ICRL significantly outperforms both traditional and deep learning based urban traffic passenger flows prediction methods.

As a primitive function of location-based services (LBSs), the location sharing aims to provide a user's current location information to other designated users. In recent years, LBSs have become one of the most popular services provided by mobile online social networks (mOSNs). As LBSs actively exploit the users' identity and current location information, appropriate approaches have to be utilized to protect the location privacy of the users. Several recent reports have discussed the significance of friendship privacy protection with the goal of hiding the friendship relation of users from unintended entities. However, to the best of our knowledge, there hasn't been an approach for protecting the location sharing with complete privacy of location and friendship connections. To address this issue, we propose a new cryptographic primitive, functional pseudonym, for location sharing in mOSNs that ensures both of them. Unlike many of the existing solutions, our approach does not require a fully trusted server and does not assume pre-established secrets among friends, and therefore is highly practical. Also, the proposed approach significantly reduces computational overhead of users by delegating part of the computations for location sharing to a server, therefore it is highly sustainable. Our primitive can be widely used in many mOSNs to enable LBSs with improved privacy and sustainability. Consequently, it will contribute to proliferate LBSs by eliminating users privacy concerns.

Fog computing, a paradigm that extends cloud computing and services to the edge of the network, meets enhanced requirements by locating data, computation power, and networking capabilities closer to end nodes. Fog computing is distinguished by its accessibility to end users, particularly its support for mobility. Fog nodes are geographically distributed and are deployed near wireless access points in areas with a significant usage. Fog devices may take the form of stand-alone servers or network devices with on-board computing capabilities. Services are hosted at the network edge or even within end-user devices, such as set-top boxes or access points. This reduces service latency, improves quality of service, and provides a superior experience for the user. Fog computing supports emerging Internet of Things (IoT) applications that demand real-time or predictable latency, such as industrial automation, transportation, and networks of sensors and actuators. Due to the capability to support a wide geographical distribution, fog computing is well positioned for real-time big data analytics. Fog supports densely distributed data collection points, adding a fourth axis to the often-mentioned big data dimensions (volume, variety, and velocity). Issues of security and privacy are in fog computing, but this remains understudied particularly in the design and implementation of fog computing; such solutions may not suit fog computing devices that are at the edges of networks. In such environments, fog computing devices face threats that do not arise in a well-managed cloud environment. The aim of this special issue in Concurrency and Computation Practice and Experience (CCPE) is to promote research and reflect the most recent advances of security and privacy issues in Fog computing. It includes invited, high-quality papers presented at the 9th International Symposium on Cyberspace Safety and Security (CSS 2017). This is also an open special issue where everyone is encouraged to submit papers. This special issue contains research papers addressing the state of the art technologies related to the security and privacy of fog computing. The set of accepted papers can be organised under the following key themes. Security has long been a critical but difficult problem to be addressed in the fog computing field. In this scheme, there are four high-quality papers accepted for publication in this special issue.1-4 First, Yu et al shared their survey work about services communication of Microservice-enabled Fog applications.1 Because a fog application based on Microservices architecture consists of numerous services and communication among services, they mainly focus on the security issues that arise in services communication of Microservices in four aspects: containers, data, permission, and network. Second, Zhao et al presented an IP geolocation method based on identification routers and local delay distribution similarity.2 IP geolocation is usually used in fog computing to avoid high latency and discriminate malicious requests by judging the location of users. Existing delay measurement-based IP geolocation approaches are not applicable to the network that has hierarchical topology and weak connectivity, and the precision of the classical Street-Level Geolocation (SLG) method will decrease dramatically when the common routers are anonymous. In this paper, the authors proposed an IP geolocation method based on identification routers and local delay distribution similarity to solve the IP geolocation problem in fog computing. Third, return-Oriented Programming (ROP) attacks become very popular in recent years as these attacks can bypass traditional defense mechanisms such as data execution prevention (DEP) effectively. Previous solutions suffer from limitations in that (1) some methods need to modify the target programs, (2) some methods introduce considerable performance cost, (3) some methods rely on the special hardware, and (4) most of existing methods could not provide an online protection for the target processes. In this paper, Tian et al presented OnRop, an on-the-fly ROP attack protection system by using the commodity hardware features and OS internal facilities.3 Their system is compatible with the existing programs, and its protection layer can be added on demand. Finally, Zhang et al proposed an algorithm to address the problems of latency in video denoising in fog computing environment. A series of measures has been applied in their algorithm, such as communication rate, and extremely heavy noise, structure registration, inter-frame and inner-frame filters, and distribution compensation. Privacy is another important issue in fog computing. In this scheme, there are also four accepted papers. First, Li et al proposed two practical approaches to implement a cloud-based DPI middlebox.5 The outsourced DPI middle-box performed payload inspection over encrypted traffic while preserving the privacy of both communication data and inspection rules. Second, Cao et al proposed an effective privacy-preserving scheme for electric load monitoring,6 which could guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behaviour model based on Factorial Hidden Markov Model (FHMM) is established. In addition, noise is added to the behaviour parameter, which is different from the traditional methods that usually add noise to the energy consumption data. Third, Zhou et al proposed effective methods to assist users to balance between the full control and the additional interaction burden,7 including sorting, recommendations, and establishing profiles. Finally, Zhang et al mainly focused on the privacy for Video Denoising.4 All these four papers have well introduced the latest research to the academia on addressing the privacy problem in fog computing. The next scheme is about the cryptography in fog computing. We accepted three papers for this scheme.8-10 First, Li et al proposed a verifiable chaotic encryption based on Chebyshev polynomials.8 The method supported verifiable function for data integrity. To further improve the efficiency of the method, a corresponding outsourced encryption method is constructed, where the heavy overhead evaluations of Chebyshev polynomials were transferred from the user side to the cloud server. The outsourced encryption also provided the checkability for data integrity and correctness of cloud computations. The method is suitable for mobile users with limited computing resources. Second, Bahrami et al proposed a novel hierarchical key pre-distribution method based on “Residual Design” for fog networks.9 The proposed key distribution method was designed to minimise storage overhead and memory consumption while increasing network scalability. The method was also designed to be secure against node capture attacks. Third, Wang et al proposed a new general pairing-free certificate-less signature method based on the variant of RSA problem and the discrete logarithm problem.10 As far as we know, this method was the first RSA-based certificate-less signature scheme that can possess resistance to Type I and Type II adversaries. There are also two papers that adopted blockchain techniques into Fog computing.11, 12 First, Huang et al proposed a new mechanism SeShare for data storing based on blockchain to realise signature uniqueness, which solved the problem of generating signatures for the same file meanwhile by different group users.11 Specifically, their method recorded every signature of a file in a blockchain in chronological order, and only one user was allowed to add new signature at the end of the blockchain when modification conflicts occurred. Second, Huang et al proposed a fair three-party contract signing protocol based on the primitive of blockchain, which could be applied to the scenario of fog computing.12 Their proposed construction allowed the participants to sign a contract in a fair way without the involvement of an arbitrator. Moreover, the privacy of the contract content could be preserved on the public chain. Their method also realised the proposed protocol through the private blockchain and provided the experimental simulation that analyses the efficiency and effectiveness. The articles presented in this special issue provides insights related to the security and privacy issues in fog computing, including blockchain-based techniques, cryptography, performance evaluation and improvements, and application developments. We wish the readers can benefit from insights of these papers, and contribute to these rapidly growing areas. Sheng Wen received his PhD degree from Deakin University, Melbourne, in October 2014. Currently, he has been working full-time as a senior lecturer (A/P in US) in Swinburne University of Technology. Before this, he first worked as a research fellow and then a Lecturer in Computer Science in the School of Information Technology at Deakin University from the year of 2015. Dr. Wen manages several research projects in the last three years. Since late 2014, Dr. Wen has received a large amount of funding from both academia and industries as co-/Chief Investigator (CI), including ARC Linkage Projects and CSRIO-Defence Joint Projects. Dr. Wen is now the Program Leader for System Security & Blockchain in Swinburne Cybersecurity Lab and Blockchain Innovation Lab. He is leading a medium-size research team in the system security area. This team includes Dr. Wen, eight Ph.D. students in Swinburne as co-/supervisors and three Honours students. Dr Wen has published over seventy fully-refereed papers in prestigious journals and leading conferences. He has also edited three books and seven journal special issues. In particular, Dr Wen has published forty fully refereed high-quality journal articles. Among these articles, fifteen articles are published in the most prestigious IEEE or ACM Transactions. Due to his outstanding performance in research, he was selected as the representative young scientist in Australia parliament (2015). Sheng is leading a blockchain project with Austrac to stop money laundering in Australia. The collaboration with Austrac is significant and has been reported in top blockchain related media like ‘Bitcoin’ website. He has also been selected as the cyber security expert in SBS channel to deliver security related knowledge and comment on security related event to the mass. His research impact not only contributes to his own academic reputation, but also promotes the reputation of Swinburne University in Australia. Aniello Castiglione received the Ph.D. degree in Computer Science from the University of Salerno, Italy. He is currently an Assistant Professor (tenured as Associate Professor) at the University of Naples “Parthenope,” Italy. Previously, he was Adjunct Professor at the University of Salerno, Italy, and at the University of Naples “Federico II.” He received the Italian national qualification as an Associate Professor of Computer Science. He published more than 210 papers in international journals and conferences. Considering his journal papers, more than 70 of them are ranked Q1 in Scopus/Scimago classification and more than 50 of them are ranked Q1 in the Clarivate Analytics/ISI-WoS classification. The international academic profile of Dr. Castiglione is spread among his 86 international co-authors who belong to 75 different institutions located in 18 countries. He served in the organization (mainly as the program chair and a TPC member) in around 230 international conferences (some of them are ranked A+/A/A- in the CORE, LiveSHINE, and Microsoft Academic international classifications). In 2014, one of his papers (published on the IEEE TDSC) has been selected as the Featured Article in the IEEE Cybersecurity Initiative. In 2018, another paper (published on the IEEE Cloud Computing) has been selected as the Featured Article in the IEEE Cloud Computing Initiative. He served as a reviewer for around 110 international journals and was the managing editor of two ISI-ranked international journals. He acted as a Guest Editor in around 20 special issues, including Future Generation Computer Systems (Elsevier), Information Sciences (Elsevier), Journal of Network and Computer Applications (Elsevier), Journal of Parallel and Distributed Computing (Elsevier), Computers & Security (Elsevier), Concurrency and Computation: Practice and Experience (Wiley), IEEE Communications Magazine, IEEE Access and served as an editor on around 10 editorial boards of international journals. His current research interests include Information Forensics, Digital Forensics, Security and Privacy on Cloud, Communication Networks, Applied Cryptography, and Sustainable Computing. Tian Wang received the B.Sc. and M.Sc. degrees in computer science from the Central South University, Changsha, China, in 2004 and 2007, respectively, and the Ph.D. degree in computer science from the City University of Hong Kong, Kowloon, Hong Kong, SAR, in 2011. He was a research assistant in the City University of Hong Kong from 2006-2008. He is currently an Associate Professor at the College of Computer Science and Technology, Huaqiao University, Xiamen, China. His research interests include wireless sensor networks, cloud computing, and fog computing. Dr. Wang manages several research projects such as the National Natural Science Foundation of China (NSFC). He has 2 patents and more than 70 technical publications in international conferences and journals in the areas of wireless sensor networks, cloud computing, and mobile computing. His papers have appeared in the prestigious journals/conferences in the domain, including IEEE TMC, IEEE TVT, ACM TOSN, Information Sciences, Computer networks, ACM Mobihoc, IEEE RTSS, IEEE MASS, IEEE ICC, and so on. He has served as publicity chair and program committee member of numerous international conferences. He serves as a publicity chair for IEEE DependSys 2016, session chair for SpaCCS 2016, track co-chair for IEEE CSS 2017, and program committee member of numerous international conferences (3PGCIC 2014, APSCC 2014, HPCC 2015, CoCoNet'15, ICA3PP 2015, WASA 2015, HPCC 2016, DependSys 2015, DependSys 2016). He is on the editorial board of International Journal of High-Performance Computing and Networking (IJHPCN). Md Zakirul Alam Bhuiyan is currently an Assistant Professor of Computer and Information Sciences at the Fordham University. Before that, he worked for Temple University USA. He also worked as a Postdoctoral Research Fellow in the School of Information Science and Engineering and the School of Software at Central South University, China. He received the Ph.D. degree and the MEng degree in Computer Science and Technology from Central South University (CSU), China, in 2013 and 2009, respectively. He received the BSc degree in Computer Science and Engineering from International Islamic University Chittagong, Bangladesh, in 2005. He is a key member of the Trusted Computing Institute of CSU, where his research interests lie in cyber-physical systems (CPS), wireless sensor network applications, fault-tolerance and reliability, and sensor-cloud computing. He was a Research Assistant at the Hong Kong Polytechnic University in 2010-2011 and a Software Engineer at international software companies. He was a recipient of a “Youth Scientific Fund 2015-2017” from NSF of China, “2012 Top-notch Ph.D. Student Award” from CSU, “2012 Hunan Province Innovative Engineering Research Fund Award,” and a recipient of the "Outstanding Master Degree Dissertation Award” at both the provincial and the university levels. His papers have appeared in the prestigious journals/conferences in the domain, including ACM TOSN, IEEE TC, IEEE TPDS, IEEE SECON, IEEE/IFIP DSN, IEEE SRDS, IEEE DCOSS, and so on. He won the “Best Paper Award” at the IEEE ISPA 2013, Melbourne, Australia, and the “Best Academic Paper Award 2012.” He was invited to serve as a Guest Editor, Workshop Chair, Publicity Chair, Program Co-Chair, TPC, and reviewer for international journal/conference proceedings. He is a member of IEEE and a member of ACM. We would like to thank all of the authors who provided valuable contributions to this special issue. We are also grateful to the Review Committee for the feedback provided to the authors, which are essential in further enhancing the papers. Finally, we would like to express our sincere gratitude to Professor Geoffrey Fox, the Editor in Chief, for providing us with this unique opportunity to present our works in the international journal of Concurrency and Computation: Practice and Experience.

Social computing and cloud computing are the major trends of technology development in recent years. With the unparalleled popularity, social networks and cloud platforms have become part of our daily lives. Users have produced big data that are beyond the ability of commonly used computer software and hardware tools to capture, manage, and process within a tolerable elapsed time. It has been widely recognized that security and privacy are the key challenges for social network and cloud services due to their scale, complexity, and heterogeneity. The goal of this special issue is to promote research on security, privacy, and social networks. Eleven papers were carefully selected from open submissions and invited from the best original presentations at the 13th International Conference on Information Security Practice and Experience (ISPEC 2017) and the Third International Symposium on Security and Privacy in Social Networks and Big Data (SocialSec 2017). These research papers address the state-of-the-art technologies related to security, privacy, and social networks. The papers are organized under the following topics: security and privacy in social network and web, big data and information security, hardware and software security, and network security. Social media has greater and greater influence on society in recent years. Hu et al1 present an interesting study on the influence of negative opinions spreading in social media during election period. Unlike existing approaches that rely on sentiment analysis and emotional words, the authors take advantage of nouns with emotional context to determine the election preference of each user more accurately. Protecting social networks from security threats and preserving user privacy are the key challenges in social network security. To protect social network users against cross-site scripting worms, Gupta et al2 propose a client-server JavaScript code rewriting-based framework. A Java-based prototype has been developed by the authors, and the authors test its malicious script alleviation capability on several web applications. Yang et al3 propose a novel privacy-preserving authentication protocol for anonymous web browsing, which help users avoid being monitored by the web server on the basis of the identity. In particular, the proposed protocol makes use of a pseudoidentity mechanism and an identity-based elliptic-curve cryptography algorithm. In the area of big data and cloud computing, secure nearest neighbor query over encrypted data is an important issue. Zhu et al4 put forward an efficient attack against the CloudBI-II scheme that is designed for resisting the collusion of cloud server and query users. Accordingly, the authors present an enhanced scheme that can resist the collusion attack. Outsourcing heavy computational tasks to cloud service providers has become popular in the cloud era. As commercial cloud service providers are not trusted, preserving the integrity of computational results becomes an important challenge. Yang et al5 propose a verifiable computation scheme that can protect the output privacy. Ciphertext-policy attribute-based encryption (CP-ABE) is widely used for data access control in cloud storage, which gives data owners direct and flexible control on access policies. Zhang et al6 present a multiauthority attribute-based encryption scheme with constant-size ciphertexts and user revocation for threshold access policy, which addresses the practical challenges in CP-ABE. The security and reliability of information transmission in vehicular ad hoc networks have attracted a lot of research efforts in recent years. Wang et al7 propose a neighborhood trustworthiness-based vehicle-to-vehicle authentication scheme, which uses cloud computing to evaluate the trustworthiness of vehicles for emergent information delivery. The security of ARM embedded devices is important as they are becoming increasingly ubiquitous. Chang et al8 propose a hardware-assisted memory isolation protection mechanism using the B method, and present an implementation of the proposed system on an ARM-based platform. Function-call graph matching is useful in binary code analysis for software security purposes. Huang et al9 propose a function-call graph matching method based on the Hungarian algorithm. The proposed method solves the maximum weight matching problem in polynomial time, which allows matching between graphs of large scale. This special issue would not be complete without covering network security. Yang et al10 use software-defined network techniques to build a moving target defense model that maps physical network elements to a considerably large address space and creates different times of validity randomly to generate mapping addresses. The proposed model helps make it more difficult for attackers to find the targets in a network. Shan et al11 propose a node importance scheme to a community-based caching scheme with network coding for information centric networking. Experimental results indicate that the proposed scheme can improve network performance including average download time, cache hit rate, and instantaneous hop reduction rate. The articles presented in this special issue report recent advances in some areas of security, privacy, and social networks, including security and privacy in social network and web, big data and information security, hardware and software security, and network security. We hope the readers can benefit from the insights of these works and make further contributions to these important and rapidly growing fields. We are grateful to the authors who submitted papers to this special issue. We would also like to thank the reviewers for their hard work and their valuable feedback to the authors. Finally, we would like to express our sincere gratitude to Professor Geoffrey Fox, the Editor in Chief, for providing the opportunity and assistance to edit this special issue in the international journal of Concurrency and Computation: Practice and Experience.

There are tremendous security concerns with patient health monitoring sensors in Internet of Things (IoT). The concerns are also realized by recent sophisticated security and privacy attacks, including data breaching, data integrity, and data collusion. Conventional solutions often offer security to patients' health monitoring data during the communication. However, they often fail to deal with complicated attacks at the time of data conversion into cipher and after the cipher transmission. In this paper, we first study privacy and security concerns with healthcare data acquisition and then transmission. Then, we propose a secure data collection scheme for IoT-based healthcare system named SecureData with the aim to tackle security concerns similar to the above. SecureData scheme is composed of four layers: 1) IoT network sensors/devices; 2) Fog layers; 3) cloud computing layer; and 4) healthcare provider layer. We mainly contribute to the first three layers. For the first two layers, SecureData includes two techniques: 1) light-weight field programmable gate array (FPGA) hardware-based cipher algorithm and 2) secret cipher share algorithm. We study KATAN algorithm and we implement and optimize it on the FPGA hardware platform, while we use the idea of secret cipher sharing technique to protect patients' data privacy. At the cloud computing layer, we apply a distributed database technique that includes a number of cloud data servers to guarantee patients' personal data privacy at the cloud computing layer. The performance of SecureData is validated through simulations with FPGA in terms of hardware frequency rate, energy cost, and computation time of all the algorithms and the results show that SecureData can be efficient when applying for protecting security risks in IoT-based healthcare.

The Internet of Things (IoT)-Cloud combines the IoT and cloud computing, which not only enhances the IoT's capability but also expands the scope of its applications. However, it exhibits significant security and efficiency problems that must be solved. Internal attacks account for a large fraction of the associated security problems, however, traditional security strategies are not capable of addressing these attacks effectively. Moreover, as repeated/similar service requirements become greater in number, the efficiency of IoT-Cloud services is seriously affected. In this paper, a novel architecture that integrates a trust evaluation mechanism and service template with a balance dynamics based on cloud and edge computing is proposed to overcome these problems. In this architecture, the edge network and the edge platform are designed in such a way as to reduce resource consumption and ensure the extensibility of trust evaluation mechanism, respectively. To improve the efficiency of IoT-Cloud services, the service parameter template is established in the cloud and the service parsing template is established in the edge platform. Moreover, the edge network can assist the edge platform in establishing service parsing templates based on the trust evaluation mechanism and meet special service requirements. The experimental results illustrate that this edge-based architecture can improve both the security and efficiency of IoT-Cloud systems.

With the development of Internet of Things (IoT) technologies, increasingly many devices are connected, and large amounts of data are produced. By offloading the computing-intensive tasks to the edge devices, cloud-based storage technology has become the mainstream. However, if the end IoT devices send all of their data to the cloud, then data privacy becomes a great issue. In this paper, we propose a new architecture for data synchronization based on fog computing. By offloading part of computing and storage work to the fog servers, the data privacy can be guaranteed. Moreover, to decrease the communication cost and reduce the latency, we design a differential synchronization algorithm. Furthermore, we extend the method by introducing Reed-Solomon code for security consideration. We prove that our architecture and algorithm really have better performance than traditional cloud-based solutions in terms of both efficiency and security through a series of experiments.

Microblog platforms have been extremely popular in the big data era due to its real-time diffusion of information. It's important to know what anomalous events are trending on the social network and be able to monitor their evolution and find related anomalies. In this paper we demonstrate RING, a real-time emerging anomaly monitoring system over microblog text streams. RING integrates our efforts on both emerging anomaly monitoring research and system research. From the anomaly monitoring perspective, RING proposes a graph analytic approach such that (1) RING is able to detect emerging anomalies at an earlier stage compared to the existing methods, (2) RING is among the first to discover emerging anomalies correlations in a streaming fashion, (3) RING is able to monitor anomaly evolutions in real-time at different time scales from minutes to months. From the system research perspective, RING (1) optimizes time-ranged keyword query performance of a full-text search engine to improve the efficiency of monitoring anomaly evolution, (2) improves the dynamic graph processing performance of Spark and implements our graph stream model on it, As a result, RING is able to process big data to the entire Weibo or Twitter text stream with linear horizontal scalability. The system clearly presents its advantages over existing systems and methods from both the event monitoring perspective and the system perspective for the emerging event monitoring task.

editorial Free Access Share on Dependability in Cyber-Physical Systems and Applications Authors: Md Zakirul Alam Bhuiyan Fordham University, New York, NY, USA Fordham University, New York, NY, USAView Profile , Sy-yen Kuo National Taiwan University, Taipei, Taiwan National Taiwan University, Taipei, TaiwanView Profile , Damian Lyons Fordham University, New York, NY, USA Fordham University, New York, NY, USAView Profile , Zili Shao The Hong Kong Polytechnic University, Hung Hom, Hong Kong The Hong Kong Polytechnic University, Hung Hom, Hong KongView Profile Authors Info & Claims ACM Transactions on Cyber-Physical SystemsVolume 3Issue 1January 2019 Article No.: 1pp 1–4https://doi.org/10.1145/3271432Published:29 September 2018Publication History 4citation680DownloadsMetricsTotal Citations4Total Downloads680Last 12 Months116Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

A mobile crowdsensing (MCS) platform motivates employing participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. In this article, we improve the profit via the data reconstruction method, which brings new challenges, because it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In particular, two Profit-driven Online Participant Selection (POPS) problems under different situations are studied in our work: (1) for S-POPS, the sensing cost of the different parts within the target area is the Same. Two mechanisms are designed to tackle this problem, including the ProSC and ProSC+. An exponential-based quality estimation method and a repetitive cross-validation algorithm are combined in the former mechanism, and the spatial distribution of selected participants are further discussed in the latter mechanism; (2) for V-POPS, the sensing cost of different parts within the target area is Various, which makes it the NP-hard problem. A heuristic mechanism called ProSCx is proposed to solve this problem, where the searching space is narrowed and both the participant quantity and distribution are optimized in each slot. Finally, we conduct comprehensive evaluations based on the real-world datasets. The experimental results demonstrate that our proposed mechanisms are more effective and efficient than baselines, selecting the participants with a larger profit for the platform.

The real-world demands of mining big data and smart data of graph structure have led to an active research of distributed graph processing. Many distributed graph processing systems [19], [22], [23] adopt a vertex-centric programming paradigm. In these systems, messages are passed between vertices to propagate the latest states. The communication efficiency and the high overhead of synchronization are two key considerations of these systems [8], [12]. In this paper, we propose a Slow Passing Fast Consuming (SPFC) approach which can effectively improve the overall performance of vertex-centric graph processing systems. In our approach, the message passing is slow but the consuming is fast. More specifically, at the message sender side, priority is given to those smart messages which contribute more to the algorithm convergence, and at the message receiver side, messages are consumed right after arriving without any delay and intermediate buffer. Besides, by using a two-phase termination check protocol, the global synchronous barrier can be completely eliminated. In addition, based on the slow message passing strategy, further performance improvement can be achieved with some accuracy loss by eliminating those messages which are less useful for algorithm convergence. We implement our approach based on Apache Giraph [1] and evaluate it on a 12-machine cluster. The experimental results show that our method can effectively reduce the amount of message traffic and achieve up to an order of magnitude performance improvement compared with Giraph and GraphLab [3].

The development of cloud computing pours great vitality into traditional wireless sensor networks (WSNs). The integration of WSNs and cloud computing has received a lot of attention from both academia and industry. However, collecting data from WSNs to cloud is not sustainable. Due to the weak communication ability of WSNs, uploading big sensed data to the cloud within the limited time becomes a bottleneck. Moreover, the limited power of sensor usually results in a short lifetime of WSNs. To solve these problems, we propose to use multiple mobile sinks (MSs) to help with data collection. We formulate a new problem which focuses on collecting data from WSNs to cloud within a limited time and this problem is proved to be NP-hard. To reduce the delivery latency caused by unreasonable task allocation, a time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties. In TASA, a non-overlapping and adjustable trajectory is projected for each MS. In addition, a minimum cost spanning tree (MST) based routing method is designed to save the transmission cost. We conduct extensive simulations to evaluate the performance of the proposed algorithm. The results show that the TASA can collect the data from WSNs to Cloud within the limited latency and optimize the energy consumption, which makes the sensor-cloud sustainable.

Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.

The concept of Social Internet of Things (SIoT) has emerged from the integration of social networking into the core of the Internet of Things (IoT). It envisions IoT objects and devices to have social interactions with each other autonomously, cooperate with other agents, and exchange information with human users and surrounding computing devices. These objects are able to sense/actuate, store, and interpret information in an opportunistic and loosely coupled fashion. The objects in the SIoT paradigm can exhibit multiple forms of social relationships derived from their collaborative activities or functional, temporal and spatial dependencies to meet a particular need of human users, which signify the difference between the SIoT domain to that of social-based mobile networks or sensor networks. The social interaction among the SIoT objects contribute a huge volume of data to be processed and used by various applications such as social VANET, social connected health, SIoT-based recommendation service, traffic service, policing, energy management etc, in the area of Smart Cities, Smart Homes, Smart Grid, and Smart Factories to satisfy human needs, interests, and objectives. Such a dynamic landscape with billions of social communities of objects and devices requires new models, theories, and approaches of interaction and collaboration, which could be established by referring to the experience that people have already gained in social networking domain over the past few years.

The session initiation protocol (SIP) is an IP-based telephony authentication mechanism for multimedia big data communications over the Internet. It is used to set up, and control voice and video calls, as well as for instant messaging. One of the concerns of this kind of open-text-based protocol is the security for user authentication. The HTTP digest-based challenge-response authentication process is used in the original SIP. However, this kind of authentication procedure is insecure and a pre-existing user configuration on the remote server is required. According to the literature, several authentication mechanisms for SIP are already devised, but none of these SIPs are robust against existing security attacks. Therefore, we design a three-factor SIP (TF-SIP) for multimedia big data communications, which is robust and flexible against existing known security issues. We show that our TF-SIP is provably secure in the random oracle model. We formally verify the mutual authentication and the freshness of the agreed session key between the user and the remote server using the BAN logic analysis. We found that the communication and computation costs are low, but the storage cost is slightly higher for our TF-SIP in comparison with other SIPs.

Nowadays, the Internet of Things (IoT) and cloud computing have become more pervasive in the context of the industry as digitization becomes a business priority for various organizations. Therefore, industries outsource their crowdsourced Industrial IoT (IIoT) data in the cloud in order to reduce the cost for sharing data and computation. However, the privacy of such crowdsourced data in this environment has attracted wide attention across the globe. Signcryption is the significant cryptographic primitive that meets both requirement of authenticity and confidentiality of crowdsourced data among users/industries, and thus, it is ideal for ensuring secure authentic data storage and transmission in industrial crowdsourcing environments. In this paper, we introduce a new identity-based signcryption (IBSC) scheme using bilinear pairing for IIoT deployment. Besides, two hard problems are studied, called as, modified bilinear Diffie-Hellman inversion (MBDHI) assumption and modified bilinear strong Diffie-Hellman (MBSDH) assumption. The rigorous security analysis demonstrates that our IBSC scheme for IIoT is provably secure based on the intractability of decisional-MBDHI and MBSDH assumptions under formal security model without considering the concept of the random oracle. The performance comparison with other signcryption schemes shows satisfactory results. Thus, our IBSC scheme is appropriate for IIoT crowdsourcing environments, and also applicable for low-bandwidth communications.

With the development of wireless communication and positioning technology, location-based services (LBSs) have been gaining tremendous popularity, due to its ability to greatly facilitate the people's daily lives. Meanwhile, it also entails the risk of location privacy disclosure. To address this issue, general solutions introduce a single trusted anonymizer between the users and the location service provider (LSP). However, a single anonymizer offers limited privacy guarantees and incurs high communication overhead in continuous LBSs. Once the anonymizer is compromised, it may put the user information in jeopardy. In this paper, we propose a dual privacy preserving (DPP) scheme in continuous LBSs to protect the users' trajectory and query privacy. Our scheme introduces multiple anonymizers between the users and LSP, and combines with Shamir threshold mechanism, dynamic pseudonym mechanism, and K-anonymity technology to improve the users' trajectory and content privacy in continuous LBSs. An anonymizer alone cannot get the users' trajectory and query contents, and it thus can be semi-trusted. Our scheme can enhance the users' privacy and effectively solve the single point of failure in single anonymizer structure. At the same time, the query authentication can guarantee the correctness of the query results. The analysis and simulation results demonstrate that the proposed scheme has the ability to protect users' trajectory and content privacy effectively, and to reduce the computation and communication overhead of the single anonymizer.

Wireless sensor networks (WSNs) play an important role in the industrial Internet of Things (IIoT) and have been widely used in many industrial fields to gather data of monitoring area. However, due to the open nature of wireless channel and resource-constrained feature of sensor nodes, how to guarantee that the sensitive sensor data can only be accessed by a valid user becomes a key challenge in IIoT environment. Some user authentication protocols for WSNs have been proposed to address this issue. However, previous works more or less have their own weaknesses, such as not providing user anonymity and other ideal functions or being vulnerable to some attacks. To provide secure communication for IIoT, a user authentication protocol scheme with privacy protection for IIoT has been proposed. The security of the proposed scheme is proved under a random oracle model, and other security discussions show that the proposed protocol is robust to various attacks. Furthermore, the comparison results with other related protocols and the simulation by NS-3 show that the proposed protocol is secure and efficient for IIoT.

As more knowledge discovery functions or sensing units for event detection are added to sensor devices in the Internet of Things (IoT), devices acquire big data that is bigger than they are able to deliver using their radios in a given time window. As a result, energy consumption for big data acquisition and transmission and real-time data processing are great challenges. In this paper, we introduce BigReduce, a low-cost IoT framework for event detection that reduces a big amount of data at the time of data acquisition and before the data transmission across the network. BigReduce works on the analysis of the frequency content of signals as they are acquired and efficiently adapts the frequency rate based on the sensitivity to a respective event, such as fire event. Instead of transmitting the entire set of acquired data, BigReduce transmits only the signals that have a high event-sensitivity. We provide a detailed algorithm for fire event sensitivity indication based on the frequency consents. Results achieved through a lab testbed show that BigReduce is able to reduce energy consumption by at least 78% and data volume by 82% in comparison to other frameworks.

Medical data collection in healthcare monitoring applications through traditional frameworks raise serious concerns of patient data privacy and security, due to numerous security threats and attacks. In this paper, we investigate the concerns with privacy protected data collection and propose a novel patient privacy protected data collection framework with the aim to provide patient data privacy. We present a new secrete sharing scheme and a share reconstruction scheme for patient data privacy. We consider a distributed database consisting of multiple edge servers and each server receives a share of the patient data. Implementation result shows that secret share generation and sharing reconstruction do not require much computation time.

Summary The mobile cloud is a highly heterogenous and constantly evolving network of numerous portable devices utilizing the powerful back‐end cloud infrastructure to overcome their severe deficiency in computing resource and offer various services such as data sharing. Inherently, in mobile cloud, the risk of user privacy invasion by the cloud operator is high. The conditional proxy re‐encryption (CPRE) is a useful concept for secure group data sharing via cloud while preserving the privacy of the shared data from any unintended third parties including the cloud operator. Unfortunately, the state‐of‐art CPRE is not particularly designed for mobile cloud environment and therefore imposes heavy burdens to the weak mobile cloud clients. This paper introduces a new CPRE scheme, namely the CPRE for mobile cloud, which utilizes the back‐end cloud to the extreme extent so that the overhead of terminals is drastically reduced. Specifically, our scheme outsources a significant amount of computation overhead caused by the following functions at terminals: (a) re‐encryption key generation, (b) condition value change, and (c) decryption, to the cloud. The proposed scheme also allows users to verify the correctness of outsourced computation under refereed delegation of computation model. Our simulation results show CPRE for mobile cloud that outperforms its existing alternatives. Copyright © 2016 John Wiley & Sons, Ltd.

Sampling rate adaptation is a critical issue in many resource-constrained networked systems, including Wireless Sensor Networks (WSNs). Existing algorithms are primarily employed to detect events such as objects or physical changes at a high, low, or fixed frequency sampling usually adapted by a central unit or a sink, therefore requiring additional resource usage. Additionally, this algorithm potentially makes a network unable to capture a dynamic change or event of interest, which therefore affects monitoring quality. This article studies the problem of a fully autonomous adaptive sampling regarding the presence of a change or event. We propose a novel scheme, termed “event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)” by addressing the problem in two stages, which leads to reduced resource usage (e.g., energy, radio bandwidth). First, e-Sampling provides the embedded algorithm to adaptive sampling that automatically switches between high- and low-frequency intervals to reduce the resource usage, while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained networks. In the absence of an event, the “uninteresting” data is not transmitted to the sink. Thus, the energy cost is further reduced. e-Sampling can be useful in a broad range of applications. We apply e-Sampling to Structural Health Monitoring (SHM) and Fire Event Monitoring (FEM), which are typical applications of high-frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in effectively expanding the capacity of WSNs for high data rate applications.

Large civil structures, such as bridges, buildings, and aerospace vehicles form the backbone of our society are critical to some catastrophic events such as damage. Wired sensor networks are usually adopted for structural health monitoring (SHM) applications. This is also an important Smart City application. Recent wireless sensor networks (WSNs) technology promises the eventual ability to cover such a structure and continuously monitor its health. However, researchers from both engineering and computer science domains face numerous hurdles, such as application-specific requirements, in reaching this goal. These hurdles have a cumulative effect on severely resource-constrained WSNs. This article provides a comprehensive investigation of WSN-based SHM applications with an emphasis on networking perspectives to get insights into a cyber-physical system (CPS) design. First , we provide the SHM philosophy and conduct extensive comparative studies regarding various aspects of benefits and hurdles of going wireless for SHM. Second , we propose a taxonomy of SHM techniques and their applicability to WSNs. Third , we show a transition from the WSN-based SHM towards the CPS design, expecting that such a design will mitigate WSN resource constraints and satisfy SHM application-specific requirements to a great extent. For each of these, we discuss a surge of existing schemes with an emphasis on limitations of the state-of-the-art, and we point out open issues. Finally , we propose a series of design guidelines for a potential CPS. This article will help both engineering and computer science domain researchers/engineers and respective communities in designing future CPS to ensure the economic benefit and public safety in functioning civil structures.

As an alternative to current wired-based networks, wireless sensor networks (WSNs) are becoming an increasingly compelling platform for engineering structural health monitoring (SHM) due to relatively low-cost, easy installation, and so forth. However, there is still an unaddressed challenge: the application-specific dependability in terms of sensor fault detection and tolerance. The dependability is also affected by a reduction on the quality of monitoring when mitigating WSN constrains (e.g., limited energy, narrow bandwidth). We address these by designing a dependable distributed WSN framework for SHM (called DependSHM) and then examining its ability to cope with sensor faults and constraints. We find evidence that faulty sensors can corrupt results of a health event (e.g., damage) in a structural system without being detected. More specifically, we bring attention to an undiscovered yet interesting fact, i.e., the real measured signals introduced by one or more faulty sensors may cause an undamaged location to be identified as damaged (false positive) or a damaged location as undamaged (false negative) diagnosis. This can be caused by faults in sensor bonding, precision degradation, amplification gain, bias, drift, noise, and so forth. In DependSHM, we present a distributed automated algorithm to detect such types of faults, and we offer an online signal reconstruction algorithm to recover from the wrong diagnosis. Through comprehensive simulations and a WSN prototype system implementation, we evaluate the effectiveness of DependSHM.

High-resolution vibration data collection with data quality guaranteeing is important in a class of applications like industrial machine and structural health monitoring. Applying wireless vibration sensor networks (WVSNs) to this class is challenging due to severe resource constraints (e.g., bandwidth and energy). State-of-the-art data reduction approaches (e.g., signal processing, in-network aggregation) suggested to improve these constraints do not satisfy application-specific requirements, e.g., high quality of data (QoD) collection or quality of monitoring (QoM). In this paper, we propose vCollector, a general approach to vibration data collection and monitoring in a resource-constrained WVSN. We enable each sensor to reduce the amount of data (before transmission) in a decentralized manner in two stages: the data acquisition stage and data transmission stage. In the first, we propose a solution to low-complexity signal processing; each sensor analyzes signals using the fast Fourier transform (FFT) under the quadrature amplitude modulation (QAM) and then applies an idea from the Goertzel algorithm (first proposed by Goertzel in 1958) so that the sensor can reduce a significant amount of data without sacrificing the QoD. In the second stage, we propose a decision-making algorithm by which each sensor can make a decision on its acquired data (considered event-sensitive data if it has information about harmful vibrations) so that event-insensitive data communication is reduced. Evaluation results (obtained by simulations using our empirical data traces and by a real system deployment) demonstrate that vCollector significantly reduces energy consumption and guarantees QoM in a WVSN.

Identifying driving anomalies is of great significance for improving driving safety. The development of the Internet-of-Vehicle (IoV) technology has made it feasible to acquire big data from multiple vehicle sensors, and such big data play a fundamental role in identifying driving anomalies. Existing approaches are mainly based on either rules or supervised learning. However, such approaches often require labeled data, which are typically not available in big data scenarios. In addition, because driving behaviors differ under vehicle statuses (e.g., speed and gear position), to precisely model driving behaviors needs to fuse multiple sources of sensor data. To address these issues, in this paper, we propose SafeDrive, an online and status-aware approach, which does not require labeled data. From a historical dataset, SafeDrive statistically offline derives a state graph (SG) as a behavior model. Then, SafeDrive splits the online data stream into segments and compares each segment with the SG. SafeDrive identifies a segment that significantly deviates from the SG as an anomaly. We evaluate SafeDrive on a cloud-based IoV platform with over 29 000 real connected vehicles. The evaluation results demonstrate that SafeDrive is capable of identifying a variety of driving anomalies effectively from a large-scale vehicle data stream with an overall accuracy of 93%; such identified driving anomalies can be used to timely alert drivers to correct their driving behaviors.

Detecting an event of interest, e.g., damage in aerospace vehicles from the continuous arriving data in Internet of Things (IoT) is challenging due to the detection quality. Traditional data mining schemes are employed to reduce data that often use metrics, association rules, and binary values for frequent patterns as indicators for finding interesting knowledge. However, these may not be directly applicable to the network due to certain constraints (communication, computation, bandwidth). We discover that, the indicators may not reveal meaningful information for event detection. In this paper, we propose a comprehensive data mining framework for event detection in IoT named DPminer, which functions in a distributed and parallel manner (data in a partitioned database processed by one or more sensor processors) and is able to extract a pattern of sensors that may have event information with a low communication cost. To achieve this, we introduce a new sensor behavioral pattern mining technique called differential sensor pattern (DSP) which considers different frequencies and values (non-binary) with a set of sensors. We present an algorithm for data preparation and then use a highly-compact data tree structure (called DP-Tree) for generating the DSP. Evaluation results show that DPminer can be very useful for networked sensing with a superior performance in terms of communication cost and detection quality compared to existing data mining schemes.

Wireless sensor networks (WSNs) are being suggested at an increasing rate for structural health monitoring (SHM). The objective is to monitor complex events (e.g., damage) in structures (e.g., an industrial machine and a high-rise building) that are usually carried out with wired-based SHM systems. However, monitoring events with a WSN deployed over large structures is challenging due to WSN constraints (high-resolution data transmission and energy) and the quality of monitoring. In this paper, we attempt to design a cyber-physical system (CPS) of structural event monitoring with WSNs and propose a novel model-based in-network decision making in the CPS named MODEM. We think of the idea of generic event detection (like target/object) schemes, and enable each sensor to sense and make a simplified local decision (0/1) on the complex events. We then think of the formation of engineering structures and find that a large physical structure consists of a number of substructures. We enable deployed sensors to be organized into groups in such a way that a groupwise final decision (e.g., 0/1) can be provided for each substructure independently so that the existence of an event (if there is any) in a specific substructure can be identified by WSNs. MODEM is fully distributed in nature, promises to have the monitoring quality similar to the original wired-based schemes, and consumes much less energy for transmissions and computations than existing schemes do. The effectiveness of MODEM is shown via both simulations and real experiments.

Traditional tracking solutions in wireless sensor networks based on fixed sensors have several critical problems. First, due to the mobility of targets, a lot of sensors have to keep being active to track targets in all potential directions, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected when moving into holes. Third, when targets stay at certain positions for a long time, sensors surrounding them have to suffer heavier work pressure than do others, which leads to a bottleneck for the entire network. To solve these problems, a few mobile sensors are introduced to follow targets directly for tracking because the energy capacity of mobile sensors is less constrained and they can detect targets closely with high tracking quality. Based on a realistic detection model, a solution of scheduling mobile sensors and fixed sensors for target tracking is proposed. Moreover, the movement path of mobile sensors has a provable performance bound compared to the optimal solution. Results of extensive simulations show that mobile sensors can improve tracking quality even if holes exist in the area and can reduce energy consumption of sensors effectively.

Abstract Most indoor wireless sensor network localization methods require costly site surveys to collect fingerprint information for later comparison. Moreover, due to the dynamic nature of fingerprint information in indoor wireless environments, the need for site surveys may be ongoing. In this work, indoor localization is addressed with an unsupervised learning algorithm. Our novel algorithm based on received signal strength combines the information conveyed by both range‐based and range‐free localization with state‐of‐art optimization techniques. A specially designed hierarchical Bayesian hidden Markov model coupled with a particle filter helps mitigate non‐line‐of‐sight and multipath errors. This grid‐based data sample process, derived from the theory of Dirichlet processes, simplifies the global optimization problem of unsupervised learning by employing a single initial hyper‐parameter. Meanwhile, for obtaining accurate coordinates of mobile nodes, a unique semidefinite programming method is used to provide feedback to the radio propagation model. This feedback step can enable the grid‐based algorithms not only to establish the coordinates of a mobile node, but also to optimize the accuracy iteratively. Theoretical and experimental analyses indicate that the proposed algorithm can achieve better localization accuracy than conventional range‐based algorithms without adding computation cost. Copyright © 2016 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) are being deployed for structural health monitoring (SHM) applications at an increasing rate. A WSN is often organized into groups or clusters for distributed monitoring purposes. However, we discover that the dependability (in terms of the monitoring ability/quality and low false alarm rate) is greatly affected by such grouping schemes, as they do not satisfy application-specific monitoring aspects. We present an SHM application oriented network architecture (SHMnet) and analyze health event monitoring performance with it. We propose a substructure-oriented sensor organization (SOSO), considering the formation of engineering structures and finding that a large physical structure consists of a number of substructures. We enable deployed sensors to be organized into groups (unlike dynamic clusters/trees) in such a way that each group of sensors can monitor a substructure independently. We evaluate SHMnet via simulations using real data traces. The evaluation results, compared to existing work, show that SHMnet achieves at least five times the energy saving (including the energy for communication) in WSNs and dependability in terms of high ability of monitoring and low false alarm rate.

Structural health monitoring (SHM) systems are implemented for structures (e.g., bridges, buildings) to monitor their operations and health status. Wireless sensor networks (WSNs) are becoming an enabling technology for SHM applications that are more prevalent and more easily deployable than traditional wired networks. However, SHM brings new challenges to WSNs: engineering-driven optimal deployment, a large volume of data, sophisticated computing, and so forth. In this paper, we address two important challenges: sensor deployment and decentralized computing. We propose a solution, to deploy wireless sensors at strategic locations to achieve the best estimates of structural health (e.g., damage) by following the widely used wired sensor system deployment approach from civil/structural engineering. We found that faults (caused by communication errors, unstable connectivity, sensor faults, etc.) in such a deployed WSN greatly affect the performance of SHM. To make the WSN resilient to the faults, we present an approach, called${\tt FTSHM}$(fault-tolerance in SHM), to repair the WSN and guarantee a specified degree of fault tolerance.${\tt FTSHM}$searches the repairing points in clusters in a distributed manner, and places a set of backup sensors at those points in such a way that still satisfies the engineering requirements.${\tt FTSHM}$also includes an SHM algorithm suitable for decentralized computing in the energy-constrained WSN, with the objective of guaranteeing that the WSN for SHM remains connected in the event of a fault, thus prolonging the WSN lifetime under connectivity and data delivery constraints. We demonstrate the advantages of${\tt FTSHM}$through extensive simulations and real experimental settings on a physical structure.

Tracking mobile targets in wireless sensor networks (WSNs) has many important applications. As it is often the case in prior work that the quality of tracking (QoT) heavily depends on high accuracy in localization or distance estimation, which is never perfect in practice. These bring a cumulative effect on tracking, e.g., target missing. Recovering from the effect and also frequent interactions between nodes and a central server result in a high energy consumption. We design a tracking scheme, named t-Tracking, aiming to achieve two major objectives: high QoT and high energy efficiency of the WSN. We propose a set of fully distributed tracking algorithms, which answer queries like whether a target remains in a “specific area” (called a “face” in localized geographic routing, defined in terms of radio connectivity and local interactions of nodes). When a target moves across a face, the nodes of the face that are close to its estimated movements compute the sequence of the target's movements and predict when the target moves to another face. The nodes answer queries from a mobile sink called the “tracker”, which follows the target along with the sequence. t-Tracking has advantages over prior work as it reduces the dependency on requiring high accuracy in localization and the frequency of interactions. It also timely solves the target missing problem caused by node failures, obstacles, etc., making the tracking robust in a highly dynamic environment. We validate its effectiveness considering the objectives in extensive simulations and in a proof-of-concept system implementation.

Mission-critical applications of wireless sensor networks (WSNs) require collecting all data from sensors without any loss. Existing hop-by-hop and end-to-end retransmissions still face challenges in data collection reliably over shortest-paths in WSNs due to unreliable links and resource-constraints (energy, bandwidth). Such paths easily break when operating environments are harsh and vary from time to time and from location to location. In this paper, we propose rSP, an approach to preserve reliable shortest-paths in a WSN considering those environments. We assume that link failures are stochastic and independent. We use an algorithm to calculate the steady-state unreliability and availability of links over shortest-paths in order to optimize the extra energy consumption for a shortest-path failure. We then propose an algorithm to find local routing path reliability (LRPR) from each sensor to its upstream sensors to preserve a shortest-path reliable. If some links around some locations at some point of time appear more vulnerable than other links, the best reliable link for that time is chosen. Simulation results based on empirical dataset show that rSP improves the reliability over 70% and the energy-efficiency in WSNs by 50% compared to well-known approaches.

Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures. Its objective is to monitor the integrity of structures and detect and pinpoint the locations of possible damages. Although wired network systems still dominate in SHM applications, it is commonly believed that wireless sensor network (WSN) systems will be deployed for SHM in the near future, due to their intrinsic advantages. However, the constraints (e.g., communication, fault tolerance, energy) of WSNs must be considered before their deployment on structures. In this article, we study the methodology of sensor placement optimization for WSN-based SHM. Sensor placement plays a vital role in SHM applications, where sensor nodes are placed on critical locations that are of civil/structural engineering importance. We design a three-phase sensor placement approach, named TPSP, aiming to achieve the following objectives: finding a high-quality placement for a given set of sensors that satisfies the engineering requirements, ensuring communication efficiency and reliability and low placement complexity, and reducing the probability of failures in a WSN. Along with the sensor placement, we enable sensor nodes to develop “connectivity trees” in such a way that maintaining structural health state and network connectivity, for example, in case of a sensor fault, can be done in a distributed manner. The trees are constructed once (unlike dynamic clusters or trees) and do not incur additional communication costs for the WSN. We optimize the performance of TPSP by considering multiple objectives: low communication cost, fault tolerance, and lifetime prolongation. We validate the effectiveness and performance of TPSP through both simulations using real datasets and a proof-of-concept system on a physical structure.

Target tracking is one of the key applications of wireless sensor networks (WSNs). Existing work mostly requires organizing groups of sensor nodes with measurements of a target's movements or accurate distance measurements from the nodes to the target, and predicting those movements. These are, however, often difficult to accurately achieve in practice, especially in the case of unpredictable environments, sensor faults, etc. In this paper, we propose a new tracking framework, called FaceTrack, which employs the nodes of a spatial region surrounding a target, called a face. Instead of predicting the target location separately in a face, we estimate the target's moving toward another face. We introduce an edge detection algorithm to generate each face further in such a way that the nodes can prepare ahead of the target's moving, which greatly helps tracking the target in a timely fashion and recovering from special cases, e.g., sensor fault, loss of tracking. Also, we develop an optimal selection algorithm to select which sensors of faces to query and to forward the tracking data. Simulation results, compared with existing work, show that FaceTrack achieves better tracking accuracy and energy efficiency. We also validate its effectiveness via a proof-of-concept system of the Imote2 sensor platform.

Wireless Sensor Networks (WSNs) are mostly deployed to detect events (i.e., objects or physical changes) at a high/low frequency sampling that is usually adapted by a central unit (or a sink), thus requiring additional resource usage in WSNs. However, the problem of autonomous adaptive sampling regarding the detection of events has not been studied before. In this paper, we propose a novel scheme, termed “event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)” by addressing the problem in two stages, which lead to reduced resource usage (e.g., energy, radio bandwidth) in WSNs. First, e-Sampling provides a solution to adaptive sampling that automatically switches between high- and low-frequency intervals to reduce the resource usage while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained WSNs. In the absence of an event, “uninteresting” data is not transmitted to the sink. We apply e-Sampling to structural health monitoring (SHM), which is a typical application of high frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in expanding the capacity of WSNs for high data rate applications.

Structural health monitoring (SHM) brings new challenges to wireless sensor networks (WSNs) : large volume of data, sophisticated computing, engineering-driven optimal deployment, and so forth. In this paper, we address two important challenges: sensor placement and decentralized computing. We propose a solution to place sensors at strategic locations to achieve the best estimates of geometric properties of a structure. To make the deployed network resilient to faults caused by communication errors, unstable network connectivity, and sensor faults, we present an approach, called FTSHM (fault tolerance in SHM), to repairing the network to guarantee a specified degree of fault tolerance. FTSHM searches the repairing points in clusters and places a set of backup sensors at those points by satisfying civil engineering requirements. FTSHM also includes a SHM algorithm suitable for decentralized computing in energy-constrained WSNs, with the objective to guarantee that the WSN for SHM remains connected in the event of a sensor fault thus prolonging the WSN lifetime under connectivity and data delivery constraints. We demonstrate the advantages of FTSHM through simulations and experiments on a real civil structure.

Wireless sensor networks (WSNs) have become an increasingly compelling platform for structural health monitoring (SHM) due to relatively low-cost, easy installation, etc. However, the challenge of effectively monitoring structural health condition (e.g., damage) under WSN constraints (e.g., limited energy, narrow bandwidth) and sensor faults has not been studied before. In this paper, we focus on tolerating sensor faults in WSN-based SHM. We design a distributed WSN framework for SHM and then examine its ability to cope with sensor faults. We bring attention to an undiscovered yet interesting fact, i.e., the real measured signals introduced by faulty sensors may cause an undamaged location to be identified as damaged (false positive) or a damaged location as undamaged (false negative) diagnosis. This can be caused by faults in sensor bonding, precision degradation, amplification gain, bias, drift, noise, and so forth. We present a distributed algorithm to detect such types of faults, and offer an online signal reconstruction algorithm to recover from the wrong diagnosis. Through simulations and a WSN prototype system, we evaluate the effectiveness of our proposed algorithms.

Fault tolerance in wireless sensor networks (WSNs) has been studied extensively by computer science researchers and they proposed many fault-tolerant schemes for various applications including target and event detection. However, these schemes would fail in a particular application of WSNs: structural health monitoring (SHM). Different from other applications of WSNs, detecting structural damage requires significant amount of civil domain knowledge and utilizes different detection model. Meanwhile, researchers in civil engineering also proposed some fault-tolerant SHM algorithms. However, these algorithms are all centralized and not applicable to resource-limited wireless sensor networks. To our best knowledge, we are the first to address fault tolerance problem in WSN-based SHM. We target faulty sensor reading, one of the most difficult types of sensor fault to be detected, and propose a fault-tolerant SHM approach. The proposed approach is lightweight and it is able to disambiguate structural damage from sensor faults. The effectiveness of the proposed approach is demonstrated through both simulation and real implementation.

The increasing capabilities and declining cost of computation and communication devices has led to an increase in the number of applications of wireless sensor networks (WSNs). One such application is target tracking. Due to the severe resource constraints in WSNs, the design of an energy- efficient target tracking algorithm with high accuracy and low computational complexity becomes a highly challenging problem. In this paper, we propose an auction-based adaptive sensor activation algorithm (AASA) for target tracking in WSNs. The cluster formation process consists of a prediction method and an auction mechanism. Based on prediction, only the nodes in the predicted region (PR) are activated and the rest of the nodes remain in sleeping mode. Through the auction mechanism, appropriate sensor nodes are chosen to form a cluster and a sensor with the biggest bid in the cluster is selected as cluster head, which guarantees load balancing. To make a trade-off between energy efficiency and tracking quality, the radius of PR and the number of members in a cluster are dynamically adjusted according to current tracking quality. Simulation results show that AASA obtains significant energy savings, decreases the target missing rate and prolongs the network lifetime.

Abstract Conventional target tracking systems are based on powerful sensor nodes, capable of detecting and locating a target in a large deployment area but most systems require high transmission power levels and a large volume of messages, much time for neighbor discovery operations, and many sensors to detect the target at a given time. We propose a two‐level cooperative and energy‐efficient tracking algorithm (CET) that reduces energy consumption by requiring only a minimum number of sensor nodes to participate in communication, transaction, and perform sensing for target tracking in wireless sensor networks. It is expected that only the nodes adjacent to the target are responsible for observing the target to save the energy consumption and extend the network lifetime as well by using a wakeup mechanism and a face‐aware routing. Through performance analysis and simulation studies, we demonstrate that CET improves target capturing speed and outperforms some existing protocols of target tracking with energy saving under certain ideal situations. Copyright © 2009 John Wiley & Sons, Ltd.

We propose target tracking with monitor and backup sensors in wireless sensor networks (TTMB) to increase the energy efficiency of the network and decrease the target capturing time while considering the effect of a target's variable velocity and direction. The approach is based on a face routing and prediction method. We use a state transition strategy, a dynamic energy consumption model, and a moving target positioning model to reduce energy consumption by requiring only a minimum number of sensor nodes to participate in communication, transaction, and sensing for target tracking. Two sensor nodes, namely, 'Monitor' and 'Backup', are employed for target tracking for each period of time. For the whole time of target tracking, a linked list of monitor and backup sensors is formed. If either monitor or backup sensor fails, this approach can still survive. Simulation results compared with existing protocols show better tracking accuracy, faster target capturing speed, and better energy efficiency.

We propose a new tracking framework by organizing nodes into a polygonal spatial neighborhood in order to detect and track unauthorized traversals in surveillance wireless sensor networks. During a tracking, the neighborhood is further constructed ahead of the target traversal, and this features a timely forwarding with guaranteed delivery property. Instead of estimating future movement and position separately in a polygon, we find an original target tracking path in a graph, and create a brink on the graph called "critical region'' by introducing a brink detection algorithm to know a target's route, and to also achieve reliable inter-node communications. In addition to the basic design, an optimal sensor selection algorithm was developed to select which sensors to query, and dynamically guide the target information to a sink. Simulation results validated that the proposed approach has better tracking accuracy, reduced localization error, and is robust to strong environment noise, while using a minimum number of sensors.