As one of data sources in cloud–edge–terminal collaboration enabled artificial intelligence of things (CETC-AIoT), the integrity and confidentiality of sensed information in the terminal side directly impact on the modeling and decision-making for CETC-AIoT. However, due to openness of transmission media among cloud, edge and terminal, it could be vulnerable to pollution attack and eavesdropping attack. Additionally, the constrained resources of some terminals make it difficult to deploy strong security schemes. In this context, how to make a tradeoff between the requirement of security and the limitation of resources needs to be explored. Therefore, toward secure and lightweight data transmission for CETC-AIoT, we propose a novel Gold sequence-based secure network coding (GS-SNC) scheme in this article. Specifically, the Gold sequence is introduced to generate the pseudo-random sequence, which is used to scramble and descramble the original information. The precoding matrix is constructed to encode and decode the scrambled information. The intermediate nodes perform the random linear network coding. The simulation results show that GS-SNC has advantages compared with double prime numbers-based secure network coding (DP-SNC) and secure practical network coding (SPOC), in terms of security, computational complexity, encryption capacity, and space overhead.

Toward green Big Data System (BDS), one of the key requirements is to save energy consumption so that the system lifetime can be prolonged. Hence, the energy evaluation metric for the measurement of energy efficiency in green BDS, is very critical. Unfortunately, most current energy evaluation metrics are based on a single factor, which might be difficult to meet the diverse application requirements. In this paper, we propose a novel two-factor energy evaluation metric (TEEM) for green BDS. Specifically, the transmission distance and the modulation level are taken into account simultaneously, generating a metric named the bit-per energy consumption (BEC). Extensive simulation results demonstrate that the system performance in energy consumption can be more effectively evaluated by BEC.

Vehicular edge computing (VEC) as a promising computing paradigm has accelerated the reformation of existing dominating computing infrastructures, enabling resource provisioning in close proximity to resource requestors. However, several challenges still exist, including efficient resource scheduling and management, dynamic wireless channel state, and limited bandwidth usage. To address these issues, we introduce the digital twin (DT) technology into VEC, enabling DTs of physical entities in VEC to achieve real-time offloading decision-making in the DT simulation cycle. In particular, we propose a DT-empowered VEC (DT-VEC) architecture, aiming to achieve efficient task offloading while considering extra latency incurred by task migration. We further put forward an efficient algorithm to minimize the response latency for all the tasks in the optimization period. The simulation results have proven that our approach outperforms the other two greedy approaches.

For the application of intelligent and green transportation systems (e.g., autonomous driving), traffic congestion is a severe challenge. So far, when traffic congestion is perceived for a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestion should be tackled with rerouting since the extra overhead (e.g., travel time, fuel consumption, and CO2 emission) caused by specific congestion might be lower than that of rerouting. Against this backdrop, a prediction-based route guidance method (PRGM) is proposed for intelligent and green transportation systems. To begin with, PRGM involves a novel hybrid and dynamic system architecture based on the collaboration of vehicle clusters and the cloud platform. Notably, a backup mechanism between adjacent cluster heads is designed to avoid the problem that the data might be lost during dynamic clustering. Furthermore, PRGM involves a novel traffic congestion control strategy, which is based on four procedures: 1) perception about traffic congestion with three indexes (i.e., speed index, dense index, and acceleration index); 2) judgment about congestion type with four defined congestion types; 3) prediction about congestion duration considering the formation of congestion (i.e., why and how the congestion is formed); and 4) route planning about vehicles considering congestion duration and the extra time overhead of rerouting. Simulations are performed, and they show that the proposed PRGM not only can perceive traffic congestion more precisely and timely but also can reduce the travel time, fuel consumption, and CO2 emission of vehicles.

Post-quantum cryptography (PQC) has become the most promising cryptographic scheme against the threat of quantum computing to conventional public-key cryptographic schemes. Saber, as the finalist in the third round of the PQC standardization procedure, presents an appealing option for embedded systems due to its high encryption efficiency and accessibility. However, side-channel attack (SCA) can easily reveal confidential information by analyzing the physical manifestations, and several works demonstrate that Saber is vulnerable to SCAs. In this work, a ciphertext comparison method for masking design based on the bitslicing technique and zerotest is proposed, which balances the tradeoff between the performance and security of comparing two arrays. The mathematical description of the proposed ciphertext comparison method is provided, and its correctness and security metrics are analyzed under the concept of PINI. Moreover, a high-order masking approach based on the state of the art, including the hash functions, centered binomial sampling, masking conversions, and proposed ciphertext comparison, is presented, using the bitslicing technique to improve throughput. As a proof of concept, the proposed implementation of Saber is on the ARM Cortex-M4. The performance results show that the runtime overhead factor of 1st-, 2nd-, and 3rd-order masking is 3.01×, 5.58×, and 8.68×, and the dynamic memory used for 1st-, 2nd-, and 3rd-order masking is 17.4kB, 24.0kB, and 30.2kB, respectively. The SCA-resilience evaluation results illustrate that the 1st-order Test Vectors Leakage Assessment (TVLA) result fails to reveal the secret key with 100,000 traces.

The internal attack launched by compromised nodes is a serious security risk in distributed networks. It is due to the openness of wireless channel and the lack of trust relationships between nodes. The trust model is an interesting approach to detect and remove these compromised nodes from the trusted routing table and maintain trust relationships in distributed networks. However, how to construct the secure dominating node to aggregate and forward the information is an enormous challenge in distributed networks. In this paper, a distributed construction algorithm of multi-layer connected dominating set is proposed for secure routing with trust models. The probabilities of nodes being compromised, combined with the trust value of the trust model, are used to construct a trusted multi-layer connected dominating set. Moreover, the impact of a node being compromised on the distributed network is quantified as loss expectation. The simulation results show that the proposed algorithm can effectively reduce the impact of nodes being compromised on the distributed network, and enhance the security of the network.

Vehicular edge computing (VEC) has gained worldwide attention in both academia and industry. Current works on VEC mainly focus on task offloading and resource allocation to improve the performance of VEC systems, but seldom consider the satisfaction level of vehicles. Whereas, the satisfaction level of vehicles has been playing an important role in stimulating vehicles to pursue better quality of experience by task offloading and service outsourcing operations. In the meanwhile, there is an inescapable fact, i.e., the task execution in VEC may fail due to various reasons, and thus it is important to incorporate the failure-resisted task offloading into the failure-prone VEC system. In this paper, we aim to maximize the satisfaction of all the vehicles, while considering the potential failures in VEC. Specifically, we model satisfaction optimization as a multiple knapsack problem and further put forward a greedy heuristic approach to solve this problem in polynomial time. Extensive simulation is carried out to validate the efficiency of our approach in terms of the optimal values and the running time. The simulation results have shown that our approach can achieve a better result compared to other benchmarks.

The fast-growing Internet of Things (IoT) have generated a vast number of IoT tasks, and these tasks are usually featured by strict response latency requirements. To cater for the time-sensitive IoT application scenarios, vehicular fog computing (VFC) can be adopted to serve the offloading requests from the IoT devices. However, current works in VFC seldom consider the task execution failures that are actually inevitable owing to limited computing resources in VFC compared to cloud computing. Hence, we strive to enhance the VFC system by incorporating the failures for task execution into our system model, which makes task offloading more general and practical. We formulate our energy consumption optimization as a mixed integer nonlinear programming problem and further put forward an iterative algorithm to solve it. We validate our approach by extensive simulation and the experimental results have proven its advantages in terms of the optimal values.

Traffic congestion is a severe challenge for intelligent transportation system (ITS). So far, if traffic congestion is perceived in a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestions should be tackled with rerouting, since the extra overhead (e.g., extra travel time, extra fuel consumption, and extra CO2 emission) caused by certain congestions might be lower than that of re-routing. Against this backdrop, an intelligent route guidance strategy is proposed, in which vehicles will trade off the extra overhead of re-routing and that of waiting in congestion. Firstly, the perceived congestion is divided into four basic types. Then, the prediction method about the duration of each congestion type is formulated. Finally, the intelligent route planning mechanism is developed. Simulations demonstrate that the proposed strategy can reduce the travel time, fuel consumption, and CO2 emission for vehicles.

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

A digital-twin (DT)-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier GAN (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the DT-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92 and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for DT-based surgical implementation.

The advent of vehicular edge computing (VEC) has generated enormous attention in recent years. It pushes the computational resources in close proximity to the data sources and thus, caters for the explosive growth of vehicular applications. Owing to the high mobility of vehicles, these applications are of latency-sensitive requirements in most cases. Accordingly, such requirements still pose a great challenge to the computing capabilities of VEC, when these applications are outsourced and executed in VEC. Against this backdrop, we propose a new mathematical model, which, respectively, generalizes the computation and communication models, and applies application-oriented caching into VEC in this article. Based on this model, a new strategy is further proposed to optimize the average response time of applications over an infinite time-slotted horizon for VEC. A long-term energy consumption constraint is imposed to guarantee the stability of the VEC system, and the Lyapunov optimization technology is adopted to tackle this constraint issue. Two greedy heuristics are put forward to help find the approximate optimal solution in the drift-plus-penalty-based algorithm. Extensive experiments have been conducted to evaluate the response time and energy consumption in the caching-assisted VEC. The simulation results have shown that the proposed strategy can dramatically optimize the average response time while satisfying the long-term energy consumption constraint.

Mobile edge computing (MEC) can provision augmented computational capacity in proximity so as to better support Industrial Internet of Things (IIoT). Tasks from the IIoT devices can be outsourced and executed at the accessible computational access point (CAP). This computing paradigm enables the computing resources much closer to the IIoT devices, and thus satisfy the stringent latency requirement of the IIoT tasks. However, existing works in MEC that focus on task offloading and resource allocation seldom consider the load balancing issue. Therefore, load balance aware task offloading strategies for IIoT devices in MEC are urgently needed. In this article, software-defined network (SDN) technology is adopted to address this issue, since the rule-based forwarding policy in SDN can help determine the most suitable offloading path and CAP for undertaking the computation. To this end, we formulate an optimization problem to minimize the response latency in the proposed SDN-assisted MEC architecture. A greedy algorithm is put forward to obtain the approximate optimal solution in polynomial time. Simulation has been carried out to evaluate the performance of the proposed approach. The simulation results reveal that our approach outstands other approaches in terms of the response latency.

Currently, increasing attention is devoted to artificial intelligence (AI) enabled software defined industrial networks (AI-SDINs). Toward energy-efficient and secure data transmission in AI-SDINs, a metric called energy-cost-per-useful-bit (ECPUB), which means energy cost of transmitting per useful bit is presented to evaluate energy efficiency and a novel energy-efficiency based secure multipath routing scheme is then put forward. Specifically, the ECPUB incorporates the utility and the law of diminishing marginal utility, for revealing the relationship among energy consumption, residual energy, and useful bits required. Moreover, in this article, an energy-efficiency based secure multipath routing scheme (E2SMR) is proposed by adopting the ECPUB and (t,n) threshold secret sharing scheme, for enhancing the security under the premise of guaranteeing energy efficiency. Extensive simulation results show that ECPUB can evaluate the energy efficiency and facilitate the balance of network load, while E2SMR can prolong the lifetime of the network and simultaneously ensure the network functionality securely.

It has been observed that the Internet of Medical Things (IoMT) is being deployed to construct varieties of intelligent platforms in medical and healthcare field, in order to comprehensively improve the quality of medical services. However, the cyber security of IoMT is facing enormous threat. Although many trust schemes are proposed to address the issue, the ignorance of aging weight in trust increases the risk of long-term attacks before being detected. In this paper, we design a dynamic aging weight scheme for trust model in IoMT. Essentially, when there are cooperative behaviors between two nodes, the aging weight can be set as large as possible to slow down the increase in the trust value of normal nodes. Once noncooperative behaviors appear, the smaller aging weight could mitigate the danger of compromised nodes. The simulation results indicate that our proposed scheme could better meet the principle of “Easy to lose” for trust.

The fast-growing Internet of Thing (IoT) has generated a vast number of tasks which need to be performed efficiently. Owing to the drawback of the sensor-to-cloud computing paradigm in IoT, mobile edge computing (MEC) has become a hot topic recently. Against this backdrop, we focus on the offloading of tasks characterized by intrinsic correlations in this paper, which have not been considered in most of existing works. For the sequential arrival of such correlated tasks, the future workload can be efficiently reduced by caching the current computational result. Specifically, we resort to the Lyapunov optimization to handle the long-term constraint on energy consumption. Simulation results reveal that our approach is superior to other approaches in the optimization of response latency and energy consumption.

The increasing amount of network devices brings significant improvement to network quality but is inevitably prone to various failures. The frequent occurrence of link failures and node failures in the real-world network, causing packet losses and delay, calls for more accurate and fast detection methods. Existing network failure detection systems focus on probes and end-to-end metrics, but are limited by overhead on bandwidth or storage. Reliance on specific deployment of monitoring systems on devices like hosts also limits the feasibility and compatibility in general network topology, ignoring the potential of transferring monitoring tasks from hosts to switches. In this paper, we propose PUFF, a passive and data-driven network failure detection system based on in-network feature collection in programmable switches and machine learning algorithms. First, PUFF explores the potential use of continuous traffic changes to detect node and link failures instead of end-to-end metrics. Second, PUFF offers a software-based prototype and compares its performance with the latest passive failure detection methods. Evaluation based on simulation on real-world topology shows that PUFF can detect nearly 90% node failures and 80% link failures with less overhead in a shorter time.

Wi-Fi-based indoor localization gained a lot of attention over recent years due to low cost and open access properties. However, existing schemes might not be applicable in the real environment if their robustness is low. This paper presents CLRS, a novel distributed Indoor Positioning System (IPS) with high robustness which uses Wi-Fi signals to divide the space twice based on Angle of Arrival (AoA) and Effective Channel State Information (ECSI). The proposed scheme trade the redundancy of Access Point (AP) quantity to improve the tolerance of data measurement error. We performed simulations as well as real-world experiments, in which simulation results proved that the theoretical average error is the least when the routers are placed vertically in our localization method while the real-world experiments proved the high accuracy and robustness of CLRS.

Mobile applications in the present have created tremendous pressure on the computational capabilities of user equipments. Against this background, mobile edge computing (MEC) has been proposed to tackle this issue, e.g., by shifting the computational workload to the edge server. We in this paper consider a caching enabled task offloading in MEC, for the sake of joint optimization of task offloading and caching. We consider both energy consumption and response latency in the optimization problem and solve the problem by an alternate optimization algorithm. Extensive experiments have been conducted to evaluate the algorithm and the simulation results have shown its advantages such as rapid response latency and powerful convergence capability.

Data credibility plays a key role in facilitating evidence-based decision making in organizations and governments (e.g., policy making). One of the key data sources is the Internet of Things (IoT) devices and systems, say within a fog environment. However, the increasing complexity and interconnectivity of such IoT and fog environments can result in security vulnerabilities (e.g., due to implementation errors or flaws in the underpinning devices or systems), which can be exploited to compromise the credibility of the data. Therefore, in this article, we propose a secure Blockchain-based scheme to guarantee the credibility of nodes and data and ensure data transmission security in the fog environment. We then demonstrate the feasibility of the proposed scheme using experiments.

This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone’s guiding images with the Green Zone’s view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F1-score, and area under the ROC curve (AUC) area of our new IoMT were 0.92, 0.98, 0.95, and 0.98, respectively, which outperforms the existing perception techniques with significantly higher accuracy performance. The model stealing also has excellent performance, with the AUC area of 0.90 in Copycat slightly lower than the original model. This study suggests a new framework in the COVID-19 diagnostic integration and opens the new research about the integration of XR and deep learning for IoMT implementation.

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic images across medical institutions is usually prohibited due to patients' privacy concerns. This causes the issue of insufficient data sets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received local model updates trained by clients without exchanging clients' local data. Nevertheless, the default setting of federated learning introduces a huge communication cost of transferring model updates and can hardly ensure model performance when severe data heterogeneity of clients exists. To improve communication efficiency and model performance, in this article, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyze medical diagnostic images. Furthermore, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion based on participating clients' training time. In addition, we summarize a category of medical diagnostic image data sets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency, and fault tolerance.

With the widespread use of online social networks, billions of pieces of information are generated every day. How to detect new topics quickly and accurately at such data scale plays a vital role in information recommendation and public opinion control. One of the basic research tasks of topic detection is how to represent a topic. The existing topic representation models do not focus on how to select better differentiated words to represent topics, are still computer-centered, and do not effectively combine human intelligence and artificial intelligence (AI). To solve these problems, this article proposes a word-distributed sensitive topic representation model (WDS-LDA) based on hybrid human-AI (H-AI). The basic idea is that the distribution of words within a topic or among different topics has a great influence on the selection of topic expression words. If a word is evenly distributed among all documents of a certain topic, it indicates that the word is the common word of all documents in the topic, and it is more suitable to represent this topic. If a word is more evenly distributed among various topics, it indicates that the word is a common word of all topics, and cannot be used for the purpose of distinguishing among topics, becoming less suitable to represent any topic. At the same time, the human cognitive ability and cognitive models are introduced into topic representation based on H-AI. We introduce the user's modification of topic expression words into the topic model representation so that the topic model can learn human wisdom and become more and more accurate. Therefore, three different weights are introduced: inside weight; outside weight; and manual adjustment weight. The inside weight describes the uniform distribution of a word in the given topic, the outside weight describes the uniform distribution of a word in all topics, and the manual adjustment weight reflects whether a word is suitable as a representative vocabulary in the past manual adjustment. Tests using Sina microblog's actual data sets show that the WDS-LDA algorithm makes the representative words more important, the distinction among different topic words higher, and effectively improves the precision of subsequent algorithms, such as topic detection and topic evolutionary analysis using the topic model.

Along with the exponentially increasing quantity of intelligent terminals connected to the Internet, the spectrum competition among users becomes more and more severe in wireless networks. The network have not the ability to satisfy all communication requirements due to the significantly increasing users and demanded rates. Energy-aware admission control has been proved to be an efficient way to tackle the infeasibility caused by the severe spectrum competition among users. However, the traditional admission control is limited by gradually removing chosen users, and pays less attention to the fairness. In this article, we elaborate the concept of the fairness in a max-min optimization problem with respect to the transmission rates, by leveraging the model of bit error rates with Q-function for general fading communications. Then, we make use of the max-min rate fairness to smartly determine the subset of users to be admitted in wireless networks. Meanwhile, the overall energy consumption is minimized and the network fairness is guaranteed. In particular, the algorithms can tackle more than one user at each iteration. Numerical evaluations show the effectiveness of the algorithms.

With the gradual advancement of smart city construction, various information systems have been widely used in smart cities. In order to obtain huge economic benefits, criminals frequently invade the information system, which leads to the increase of malware. Malware attacks not only seriously infringe on the legitimate rights and interests of users, but also cause huge economic losses. Signature-based malware detection algorithms can only detect known malware, and are susceptible to evasion techniques such as binary obfuscation. Behavior-based malware detection methods can solve this problem well. Although there are some malware behavior analysis works, they may ignore semantic information in the malware API call sequence. In this paper, we design a joint framework based on local and global features for malware detection to solve the problem of network security of smart cities, called LGMal, which combines the stacked convolutional neural network and graph convolutional networks. Specially, the stacked convolutional neural network is used to learn API call sequence information to capture local semantic features and the graph convolutional networks is used to learn API call semantic graph structure information to capture global semantic features. Experiments on Alibaba Cloud Security Malware Detection datasets show that the joint framework gets better results. The experimental results show that the precision is 87.76%, the recall is 88.08%, and the F1-measure is 87.79%. We hope this paper can provide a useful way for malware detection and protect the network security of smart city.

We in this paper consider a scenario where multiple vehicles jointly provision computing resources to obtain their benefits in the contexts of vehicular fog computing. A community that vehicles can freely join and leave is sponsored by a road side unit (RSU) and thus a resource pool is established such that tasks can be performed by sufficient computing resources. RSU as a coordinator takes in charge of decision making for task scheduling. A permutation of community members is established in advance and updated periodically so as to make the most suitable decision. A task scheduling strategy is proposed from the perspective of service oriented architecture. We have carried out the experiments to investigate our approach and the experimental results have revealed our approach has a great advantage over other approaches in terms of pursuing the values of the community.

Internet of Medical Things (IoMT), intended for real-time health monitoring, are generating quantity of health data such as electrocardiogram, oxygen saturation, and blood pressure every second. The captured data should be processed and analyzed in a delay sensitive way which is vital to the survival rate for cardiovascular and cerebrovascular diseases. In this regard, Unmanned Aerial Vehicles (UAVs) have already demonstrated the enormous potentials. To begin with, due to better line-of-sight, wider communication and more flexible on-demand deployment, UAVs can realize seamless wireless connection to IoMT. Furthermore, UAVs can act as fog nodes to provision services for IoMTs such as task performing and data analysis. We in this paper focus on a sub-problem, i.e., the placement of UAVs over the serving area when they function as fog nodes. In the airborne fog computing, the placement of UAVs has an important influence on energy consumption and exploration area, let alone the communication coverage of the personal health devices on the ground. Therefore, we in this paper propose a particle swarm optimization (PSO) based algorithm to optimize the UAV placement over the serving area for the IoMT devices. We have conducted extensive simulations to evaluate it. The results show that our approach can significantly reduce the number of UAVs needed to deploy while considering the communication coverage and other factors.

Vehicular edge computing (VEC) brings the computing resources to the edge of the networks and thus provisions better computing services to the vehicles in terms of response latency. Meanwhile, the edge server can earn their revenues by leasing the computing resources. However, a higher price does not always bring forth more benefits for the edge server in VEC, since it may discourage vehicles from renting more computing resources from VEC. To the best of our knowledge, few of previous works have focused on the real-time pricing problem for VEC. We investigate in this paper the pricing problem from the viewpoints of both vehicles and the edge server, so as to optimize the utility values and revenues of vehicles and the edge server, respectively. We resort to the Stackelberg game for modeling the interactions between vehicles and edge server, and a distributed algorithm for this pricing problem is proposed in the paper. Experimental results have displayed the efficiency and effectiveness of the proposed algorithm.

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

These redundant data in IoT should be compressed or removed. Furthermore, the unstructured data in IoT plays an important role for analyzing the user behaviors, while transmitting and processing these unstructured data consumption of substantial energy. These unstructured data should be mined or restructured. In addition, the increasing number of users in IoT leads to a fast-growing data in IoT, while the Quality of Service (QoS) of IoT should be maintained regardless of the number of IoT users. Therefore, the data transmission and processing in IoT should be performed in a more intelligent manner. All these observations indicate that the integration of big data and artificial intelligence (AI) for IoT is a good propellant to improve the data transmission and processing in IoT, since big data technology (e.g., data integration, data mining, data prediction) could effectively handle various data while AI technology could further facilitate capturing and structuring big data.

The development of Internet of Things (IoT) dramatically facilitates the integration of computing systems with the physical world. However, as IoT devices are more easy to compromise than desktop computers, cybercriminals have founded IoT-based botnets to launch Distributed Denial of Service (DDoS) attacks with unprecedented traffic volume. To mitigate the damages associated with these attacks, the detection of IoT-based botnet has to preempt the command and control (C&C) communication to prevent the delivery of the attack codes. Motivated by the extensively implementation of domain generation algorithm in botnets, in this article, we propose ConnSpoiler, a lightweight system that detects IoT-based botnets by identifying the stream of algorithmically generated domains (AGDs) in a fast way. ConnSpoiler only needs negligible system resources to take effect and thus can execute well on the resource-restraint IoT devices. By outfitting a powerful statistical algorithm, i.e., threshold random walk, ConnSpoiler has a high probability (about 94%) of detecting infection before the compromised devices connect C&C servers, which can help to prevent the succeeding attacks. Moreover, ConnSpoiler only requires the benign domains to take effect and therefore does not need extra effort to label malicious samples for training phase. We evaluate ConnSpoiler based on real-world DNS traffics collected from two different large ISP networks and show that it accurately identifies devices that are compromised by unknown botnets.

Offloading cellular traffic through Opportunistic Mobile Networks, also known as opportunistic offloading has been proposed as a promising way to relieve the overload of cellular networks. The efficiency of such opportunistic offloading is highly determined by the selection of initial seeds. With considering both the freshness of the content and the cost of transmission from the cellular network to the initial seeds, this paper defines a novel freshness-aware seed selection optimization problem to find both the optimal number of initial seeds and the maximum overall content utility. To solve the optimization problem, the optimal strategy is first analyzed, and then two seed selection methods: the greedy seed selection method and the decay-based seed selection method are proposed to find the optimal number of initial seeds to maximize the overall content utility. The greedy seed selection method iteratively selects nodes with the maximum Freshness Centrality value as initial seeds. To further improve the performance, the decay-based seed selection method selects initial seeds who are far apart and important in theirs local structure. Extensive real trace-driven simulations are conducted to evaluate the performance of our proposed seed selection methods. The results show that as expected the proposed decay-based seed selection method is superior to the proposed greedy seed selection method and the random seed selection method, not only in the Infocom 06 trace, but also in the MIT Reality trace.

The burgeoning technology of Mobile Edge Computing (MEC) is attracting the traditional Location-Based Service (LBS) and Location Service (LS) to deploy due to its nature characters such as low latency and location awareness. Although this transplant will avoid the location privacy threat from the central cloud provider, there still exist the privacy concerns in the LS of MEC scenario. Location privacy threat arises during the procedure of the fingerprint localization, and the previous studies on location privacy are ineffective because of the different threat model and information semantic. To address the location privacy in MEC environment, we designed LoPEC, a novel and effective scheme for protecting location privacy for the MEC devices. By the proper model of the Radio Access Network (RAN) access points, we proposed the noise-addition method for the fingerprint data, and successfully induce the attacker from recognizing the real location. Our evaluation proves that LoPEC effectively prevents the attacker from obtaining the user's location precisely in both single-point and trajectory scenarios.

In addition to military applications, Unmanned Aerial Vehicles (UAVs) have attracted more and more attention in civilian applications such as the post-disaster relief assistance. Indeed, advantages including better line-of-sight (LOS), wider communication range and more flexible on-demand deployment make UAVs play a unique role in rescue and disaster scenarios. Emergency tasks assigned to UAVs such as people search and rescue usually require real-time responses, since it is a life-and-death matter regarding the post-disaster relief. Considering the limited computing resources and harsh energy supply replenishment for UAVs in the post-disaster relief operations, we in this paper propose a hybrid fog computing paradigm called H-FVFC that integrates UAVs and vehicular fog computing (VFC) to run the highly demanding tasks with strict latency requirements. An architecture of H-FVFC consisting of three layers is proposed and investigated in this paper, with hope to explore the possibilities of applying this computing paradigm to post-disaster relief operations. Experiments are carried out to evaluate the task offloading in H-FVFC compared to UAV-to-Cloud scheduling strategy. The results show that task offloading in the UAV-to-Vehicle way can significantly reduce the response latency. Issues not addressed in this paper are also discussed with purpose of providing some insights to the application of integration of UAV and fog-enabled vehicle in the post-disaster relief.

In resource exchange networks, nodes or agents may cooperate or compete with each other to maximize their own profits. In competitive networks, they may determine their exchange strategy selfishly, which makes it a challenge problem to design fair and efficient cooperation strategy. A popular resource exchanging mechanism called Max-Min Fairness-Based Coalitional (MMFC) is proposed in [1] to solve the problem in competitive networks, however it is still an open problem whether the mechanism is trustful if an agent lies about its resource information. In this paper, we demonstrate the trustfulness of the MMFC mechanism; combining theoretical analyses and numerical examples, we show that an agent cannot gain more benefit by misreporting its resource information.

In cyber-physical social systems (CPSS), a group of volunteers report data about the physical environment through their cyber devices and data aggregation is widely utilized. An important issue in data aggregation for CPSS is to protect users’ privacy. In this article, we use bitwise XOR and propose a bit-choosing algorithm to realize privacy-preserving min, k -th min, and percentile computation. By our algorithm, the aggregator can confirm whether a user’s data value is equal to certain value or within certain scale. Consequently, it is also possible to count the number of users satisfying given conditions. Our bit-choosing algorithm makes sure that the users send non-repetition replies to the aggregator to raise the aggregation accuracy. We analyze the communication cost and the achievable accuracy of our algorithm. Via performance comparison against existing protocols, the efficiency and accuracy of our algorithm are verified.

Edge computing provides high-class intelligent services and computing capabilities at the edge of the networks. The aim is to ease the backhaul impacts and offer an improved user experience. However, the edge artificial intelligence exacerbates the security of the cloud computing environment due to the dissociation of data, access control, and service stages. In order to prevent users from carrying out lateral movement attacks in an edge-cloud computing environment, in this paper we propose a real-time lateral movement detection method, named CloudSEC, based on an evidence reasoning network for the edge-cloud environment. First, the concept of vulnerability correlation is introduced. Based on the vulnerability knowledge and environmental information of the network system, the evidence reasoning network is constructed, and the lateral movement reasoning ability provided by the evidence reasoning network is then used. The experiment results show that CloudSEC provides a strong guarantee for the rapid and effective evidence investigation, as well as real-time attack detection.

Adapting the data rate is an important performance in mobile cloud networking, especially for the fast growth of intelligent terminals. We study a max-min fairness problem for the mobile cloud networking to guarantee the minimal transmit data rate, by leveraging the bit error rate (BER) with Q-function for modeling achievable data rates. We propose a distributed power control algorithm to obtain the optimal solution. Then, we address a total power minimization problem with the given rate requirement constraints. When there are plenty of users and excessive interferences, its feasibility issue is solved by making use of the max-min fairness of the networks. We propose a dynamic algorithm that adapts the rate requirements to minimize the total energy consumption and to simultaneously provide fairness guarantees. Numerical simulations show the efficient performance of the proposed algorithms.

Healthcare-oriented wireless sensor network (HWSN) is one of the applications of wireless sensor networks in e-health. It not only can better achieve the physiological information of people, but also more efficiently reduce the Iatency regarding information collection and transmission. However, similar to other distributed networks, it also faces enormous security challenges, especially from internal attacks. It is difficult to distinguish many attack behaviors from interference in the complex healthcare scenarios, such as On-Off attack. In this paper, we propose a Binomial Distribution-based Trust Management Scheme (BDTMS) for HWSN. The proposed method can rapidly detect and effectively defend against On-Off attacks. In addition, the proposed method is also applicable to defending against bad mouthing attacks. Simulation results show that, compared with the Time-window-based Resilient Trust Management Scheme (TRTMS), our proposed BDTMS achieves better performance in defending against On-Off attack under obstacle movement, especially with higher detection accuracy.

Offloading traffic through Opportunistic Mobile Networks, also known as opportunistic offloading is a promising way to relieve the overload of cellular networks. The efficiency of such opportunistic offloading is highly determined by the selection of initial seeds. With considering both the freshness of the content and the cost of transmission from the cellular network to the initial seeds, this paper defines a novel freshness-aware seed selection optimization problem to find K initial seeds to maximize the overall content utility. To solve the optimization problem, we propose two seed selection methods: the greedy seed selection method and the decay-based seed selection method. The greedy seed selection method iteratively selects nodes with the maximum Freshness Centrality value as initial seeds. To further improve the performance, the decay-based seed selection method selects initial seeds who are far apart and important in theirs local structure. Extensive real trace-driven simulations are conducted to evaluate the performance of our proposed seed selection methods. The results show that as expected the proposed decay-based seed selection method is superior to the proposed greedy seed selection method and the random seed selection method.

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

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

One important issue in the study of Mobile Social Networks (MSNs) is to measure the centrality (importance) of nodes in networks. However, when measuring the centrality metrics in a certain time interval, the current studies in MSNs focus on analyzing static aggregation networks that do not change over time. Actually, network topology in MSNs is changing very rapidly, which is driven by natural social behavior of people. Therefore, it will not be accurate if the static aggregation network graph is used to measure centrality metrics in a period of time. In this paper, to solve this problem, we first introduce a time-ordered aggregation model, which reduces a dynamic network to a series of time-ordered networks. Then, we propose three particular time-ordered aggregation methods to measure the centrality of nodes in a certain period under two widely used centrality metrics, namely Betweenness centrality and Degree centrality. Finally, extensive trace-driven simulations are conducted to evaluate the performance of different aggregation methods. The results show that the time-ordered aggregation methods can measure the Betweenness and Degree centrality in a time interval more accurately than the Static Aggregation Method, and the Exponential Time-ordered Aggregation Method performs much better than other aggregation methods. Therefore, we recommend to use the Exponential Time-ordered Aggregation Method to measure centrality metrics in a certain time interval.

Summary Extracting secret keys from the common randomness of wireless channels has attracted prominent attention recently. Orthogonal frequency‐division multiplexing (OFDM) systems can provide extra randomness in view of the use of multiple subchannels. So far, the secret key capacity of OFDM systems is still an open issue. In this paper, the secret key capacity of OFDM systems based on the subchannel state information is analyzed, and an expression of the secret key capacity is derived under the assumption that the subchannels are independent. To increase the secret key capacity, a power allocation scheme based on geometric program is proposed. Furthermore, an underlying propagation protocol is designed to realize the power allocation scheme. Performance simulations show that the proposed scheme achieves greater secret key capacity in comparison with equal power allocation scheme, especially at low signal‐to‐noise ratio region. Besides, the secret key bits mismatch rate during the secret key generation based on the power allocated subchannels is decreased.

Summary By applying the cloud manufacturing paradigm in the regional enterprise cluster, the enterprises or facilities may collaborate extensively for efficiently utilizing the manufacturing resources. It is valuable to explore and design the strategies of facilities selection for the autonomic control of the collaboration behaviors in production. In this paper, we model the collaboration relations in the regional enterprise cluster as a generalized social collaboration network and explore the dynamic growth process of the Facilities Collaboration Network for different strategies of facilities selection, including the random selection with and without preference, and the balanced selection with and without preference. With performance indexes such as network size, the distribution of node degree and act degree, clustering coefficient, the average shortest distance, and the number of n‐cliques, we present and analyze the characteristics of these strategies for cloud manufacturing. Next, based on these characteristics, we propose 2 mechanisms for self‐optimization in facilities collaboration, including the dynamic weighing of facilities and the concentrated processing of successive subtasks in the process. We also analyze the mechanisms' effects on the characteristics of Facilities Collaboration Network and the performance in manufacturing. Copyright © 2016 John Wiley & Sons, Ltd.

With the rapid advances of sensing technologies and wireless communications, large amounts of dynamic data pertaining to industrial production are being collected from many sensor nodes deployed in the industrial Internet of Things. Analyzing those data effectively can help to improve the industrial services and mitigate the system unprepared breakdowns. As an important technique of data analysis, clustering attempts to find the underlying pattern structures embedded in unlabeled information. Unfortunately, most of the current clustering techniques that could only deal with static data become infeasible to cluster a significant volume of data in the dynamic industrial applications. To tackle this problem, an incremental clustering algorithm by fast finding and searching of density peaks based on k-mediods is proposed in this paper. In the proposed algorithm, two cluster operations, namely cluster creating and cluster merging, are defined to integrate the current pattern into the previous one for the final clustering result, and k-mediods is employed to modify the clustering centers according to the new arriving objects. Finally, experiments are conducted to validate the proposed scheme on three popular UCI datasets and two real datasets collected from industrial Internet of Things in terms of clustering accuracy and computational time.

The advances of network technology and mobile communication technology are making eHealth possible. In eHealth systems, physiological data and relevant context-aware data are acquired continuously and in real time. At the same time, such large-scale data results in huge challenges in the aspect of real-time big data processing since eHealth data appears in the form of data stream. Therefore, we propose a novel incremental learning algorithm, namely α-SVMSGD, which improves the SVMSGD (Support Vector Machine-Stochastic Gradient Descent) algorithm by updating the training data with the continuous data stream. Besides, this α-SVMSGD may handle the problem that original SVMSGD cannot further mine the useful information in unclassified data. In α-SVMSGD, the process of training data updating is completed by introducing the concept of forgetting mechanism, in which the forgetting factor α is introduced to weed out useless training data. α-SVMSGD is applied into ambient assisted living communications, and further incorporated into the data filtering layer of a local data processing architecture (LDPA) to reduce data redundancy. Simulation results confirm that the proposed algorithm is a promising data redundancy solution for classification without loss of accuracy in the case of real-time data stream.

Traditional task solution schemes in Wireless Sensor Networks (WSNs) are mainly not suitable for complicated task processing due to high energy consumption and long processing delay. In this paper, we proposed an energy efficient Complicated Task Solution scheme for real-time task processing based on node Cooperation (CTSC), which consists of two main phases: task grouping and task allocation. In the task grouping phase, complicated tasks are divided into different groups based on task graph. In the task allocation phase, based on node cooperation, different group tasks are allocated to different nodes by using bid invitation. Thus, multiple tasks can be processed in parallel, which ultimately reduces task processing delay and limits communication overheads. Simulation results show that CTSC is much more suitable for large scale WSNs. In addition, CTSC outperforms related works in terms of shorter response time of task processing and less energy consumption.

This paper studies the practically important problem of top-k queries, which is to find the top k largest categories and their corresponding sizes. In this paper, we propose a Top-k Query (TKQ) protocol and a technique that we call Segmented Perfect Hashing (SPH) for optimizing TKQ. Specifically, TKQ is based on the framed slotted Aloha protocol. Each tag responds to the reader with a Single-One Geometric (SOG) string using the ON-OFF Keying modulation. TKQ leverages the length of continuous leading 1s in the combined signal to estimate the corresponding category size. TKQ can quickly eliminate the sufficiently small categories, and only needs to focus on a limited number of large-size categories that require more accurate estimation. We conduct rigorous analysis to guarantee the predefined accuracy constraints. To further improve time-efficiency, we propose the SPH scheme, which improves the average frame utilization of TKQ from 36.8% to nearly 100% by establishing a bijective mapping between tag categories and slots. To minimize the overall time cost, we optimize the key parameter that trades off between communication cost and computation cost. Experimental results show that our TKQ+SPH protocol not only achieves the required accuracy constraints, but also achieves a 2.6~7x faster speed than the existing protocols.

The blind rendezvous, which requires neither Common Control Channel (CCC) nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis and computer simulations have validated our algorithm.

Machine-to-Machine (M2M) mobile networks are distributed systems which include various actuators and sensors. In terms of the security of M2M mobile networks, one very significant issue is the security of Sensor Networks (SNs). Particularly, the security of transferring data from sensors to their destinations is very critical. In this paper, focusing on intrusion detection techniques, we propose an attack-defense game model to detect malicious nodes using a repeated game approach. In the proposed game model, attackers and defenders make different strategies to achieve optimal payoffs. The existences of pure nash equilibrium and mixed nash equilibrium are analyzed and proved. In the Intrusion Detection System (IDS), a game tree model is introduced to solve the error detection and missing detection problems. Simulation results present that the proposed model can reduce energy consumption by up to 50% compared with the All Monitor (AM) model, and improve the detection rate by up to 10-15% compared with the Cluster Head (CH) monitor model.

Abstract Wireless sensor network of regular topology is efficient in area covering and targets locating. However, communications with fixed channels lead to low spectrum efficiency and high probability of conflicts. This paper proposes economical timeslots‐and‐channels allocation methods for scheduling links in square, triangle, and hexagon lattice topologies. Based on these scheduling methods in square lattice, the authors explore routing methods for load balance and delay minimization, respectively, and compare their effects on transmission delay and energy consumption. The OMNet++‐based simulation for square lattice verified the effectiveness of scheduling methods for improving network throughput and made performance comparison among different scheduling methods. It also proved that delay minimization‐oriented routing helps to reduce the energy consumption for node standing by and load balance‐oriented routing helps to reduce the energy consumption for packets transmission. However, there is trade‐off between the reductions of the two types of energy consumptions. The authors further propose the idea of hybrid routing with the two aforementioned routing methods for reducing overall energy consumption and explore the challenges and countermeasures for hybrid routing optimization. Copyright © 2014 John Wiley & Sons, Ltd.

Millions of people are severely injured or killed in road accidents every year and most of these accidents are caused by human error. Fatigue and negative emotions such as anger adversely affect driver performance, thereby increasing the risk involved in driving. Research has shown that listening to the right kind of music in these situations can ameliorate driver performance and improve road safety. Context-aware music delivery systems succeed in delivering suitable music according to the situation through the process of music mood-mapping which identifies the mood of a song. Additionally, we can leverage the power of the cloud to enable crowd sensing of the mood-mapping of various songs and enhance the effectiveness of situation-aware music delivery for drivers. The cloud can be used to aggregate the crowd sensed music mood-mapping data and improve the effectiveness of music delivery by providing accurate mood-mappings from the aggregated data. Currently, context-aware music delivery systems consider only features from the song for music mood-mapping. In this paper, we propose a novel approach to music mood-mapping for drivers which also incorporates the social context of a driver including age, gender and cultural background to enhance the effectiveness of music delivery in context-aware music recommendation systems for drivers.

Recently, induced by incorporating the ubiquitous data gathering capability of wireless sensor networks (WSNs) as well as the powerful data storage and data processing abilities of cloud computing (CC), WSN-CC integration is attracting growing interest from both academia and industry. However, WSN-CC integration is still in its infancy and a lot of research efforts are expected to emerge in this area. Towards WSN-CC integration, this paper first presents four ignored research issues about WSN-CC integration. Further, our accomplished work and ongoing work regarding solving the identified research issues are briefly described. The analytical and experimental results conducted in our work show that the approaches proposed can effectively relieve the corresponding research problem. We hope our work can attract more researches into WSN-CC integration to make it develop faster and better.

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

Coordination among users is inevitable in wireless communication for efficient medium access. Even though the data rate of individual user increases significantly, the performance of wireless network does not grow up accordingly due to the high MAC coordination overhead. In this paper, we present VFA, namely virtual frame aggregation, to achieve high coordination efficiency by amortizing the overhead over multiple transmissions. VFA provides a novel way to construct a winner cluster and allow the winners to transmit without interruption. Specifically, in a multicarrier network, every contending node chooses a subcarrier and the nodes are ordered by the index of the chosen subcarrier. When there are some subcarriers chosen by two or more nodes, an additional slot is exploited to reorder the collided nodes. Finally, all ordered nodes form a cluster and the transmissions are issued sequentially and uninterruptedly. Simulation results show that usually two slots are enough to construct a sufficiently large winner cluster. Moreover, VFA achieves a notable throughput gain over IEEE 802.11 as high as 120% with better fairness under various scenarios.

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

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

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

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

Induced by incorporating the powerful data storage and data processing abilities of cloud computing (CC) as well as ubiquitous data gathering capability of wireless sensor networks (WSNs), CC-WSN integration received a lot of attention from both academia and industry. However, authentication as well as trust and reputation calculation and management of cloud service providers (CSPs) and sensor network providers (SNPs) are two very critical and barely explored issues for this new paradigm. To fill the gap, this paper proposes a novel authenticated trust and reputation calculation and management (ATRCM) system for CC-WSN integration. Considering the authenticity of CSP and SNP, the attribute requirement of cloud service user (CSU) and CSP, the cost, trust, and reputation of the service of CSP and SNP, the proposed ATRCM system achieves the following three functions: 1) authenticating CSP and SNP to avoid malicious impersonation attacks; 2) calculating and managing trust and reputation regarding the service of CSP and SNP; and 3) helping CSU choose desirable CSP and assisting CSP in selecting appropriate SNP. Detailed analysis and design as well as further functionality evaluation results are presented to demonstrate the effectiveness of ATRCM, followed with system security analysis.

The powerful data storage and data processing abilities of cloud computing (CC) and the ubiquitous data gathering capability of wireless sensor network (WSN) complement each other in CC-WSN integration, which is attracting growing interest from both academia and industry. However, job scheduling for CC integrated with WSN is a critical and unexplored topic. To fill this gap, this paper first analyzes the characteristics of job scheduling with respect to CC-WSN integration and then studies two traditional and popular job scheduling algorithms (i.e., Min-Min and Max-Min). Further, two novel job scheduling algorithms, namely priority-based two phase Min-Min (PTMM) and priority-based two phase Max-Min (PTAM), are proposed for CC integrated with WSN. Extensive experimental results show that PTMM and PTAM achieve shorter expected completion time than Min-Min and Max-Min, for CC integrated with WSN.

The nodes near sink in wireless sensor networks are featured with heavy transmission loads, so these performance bottleneck nodes are prone to running out of energy early with shortened network lifetime. Routing tactic plays important role on transmission performance and energy consumption of network. In this paper, we enumerate typical source routing tactics in Grid topology, include 1) Random Equal Probability Forwarding, 2) Line Forwarding, 3) Source-Zigzag Forwarding, 4) Destination-Zigzag Forwarding, and 5) Balance Forwarding. We analysis the traffic load distribution among nodes for these routing tactics, and calculate the load of performance bottleneck nodes and transmission delay in one data collection cycle. Based on the energy consumption model, we further make comparison on the energy consumptions of transmission and standing-by for these tactics. Numerical results show that Balance Forwarding tactic leads to the least overall energy consumption on performance bottleneck nodes, and Line Forwarding and Random Equal-Probability Forwarding lead to high energy consumption.

Technologies to support the Machine-to-Machine (M2M) is becoming more important as the need to better understand our environments and make them smart increases. As a result it is predicted that intelligent devices and networks, such as wireless network, will not be isolated but connected and integrated composing computer networks. So far, to enable an End-to-end M2M service, WebSocket has attracted lots of attentions because of its unique full-duplex communications features. Besides, ZigBee technology has widely been deployed in short-range wireless communication systems with its low-power dissipation and high transmission speed. In this paper, we focus on the emerging M2M gateway development for home and industry applications. Specifically, by providing the detailed system architecture and user cases, we give a specific analysis on environmental monitoring implemented with WebSocket and ZigBee technology. The ZigBee sensor network is used to collect the temperature and humidity information. The foreground of the system shows the related data through B/S (Browser/Server) mode by utilizing WebSocket to push the information received by a web server to the client browser.

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

By incorporating the advantages of cloud computing (CC) and wireless sensor networks (WSNs), the integration of CC and WSNs attracts a lot of attention from both academia and industry. However, trust and reputation management for CC and WSNs integration is a critical and barely explored issue, which could strongly prevent the cloud service users (CSUs) from choosing the desirable cloud service providers (CSPs) or hinder the CSP from selecting appropriate sensor network providers (SNPs). To fill the gap, this paper proposes a novel trust and reputation management system for CC and WSNs integration. Considering the attribute requirement of CSU and CSP as well as the cost, trust and reputation of the service of CSP and SNP, the proposed system achieves the following two goals: 1) calculate and manage the trust and reputation regarding the service of CSP and SNP; 2) help CSU choose CSP and assist CSP in choosing SNP. Evaluation results are also shown to verify effectiveness of the proposed system.

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

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

Wireless sensor networks (WSNs) receive a lot of attention because of their great potential in monitoring the physical or environmental conditions of military, industry, and civilian. Moreover, mobile cloud computing (MCC) is widely focused, as they can greatly alleviate the hardware limit of mobile devices as well as enable a lot of new mobile applications. All these make the integration of WSNs and MCC a very hot research topic. In this paper, we first observe a context non-awareness issue between mobile user and WSNs, which affects the mobile user obtaining the desirable data when integrating WSNs and MCC. Then focusing on solving the context non-awareness issue to provide desirable data to mobile users, we propose a novel framework for integrating WSNs and MCC. The proposed framework performs data recommendation, data prediction as well as data traffic monitoring in the cloud to obtain the data feature information required by the mobile users and potential status of WSNs. Then these user data feature information and potential WSNs status information are utilized to optimize the deployment of WSNs and check the status of WSNs. This could in turn offer the desirable data to the mobile users. Extensive evaluations also validate the effectiveness of the proposed framework.

Micro-blog marketing has become a main business model for social networks nowadays. On social networking sites (e.g., Twitter), micro-blog marketing enables the advertisers to put ads to attract customers to buy their products. During this process, a rather key step for the success of advertisers is to conduct marketing researches to discover which micro-blog users are their potential customers who can greatly promote their products to other customers so that the advertising investment can be greatly reduced. This problem is considered as “influence maximization” issue. In this paper and in attempt to discover the influential users in micro-blog marketing, we try to analyze the influences of nodes in a micro-blog network and propose a Community Scale-Sensitive Maxdegree (CSSM) algorithm for maximizing the influences when placing ads. Experimental results on the very hot micro-blog service (i.e., Twitter dataset) demonstrate that our proposed CSSM algorithm significantly outperforms other related node selection strategies, in terms of the influence spread and time complexity.

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

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

The internal delay methods for sequential equivalent sampling are widely used in the digital storage oscilloscope and limited completely by maximum operating frequency of embedded system. A new external delay technology for equivalent sampling oscilloscope is presented in this paper. The technology is based on the external programmable delay chips, which provide much shorter delay time for equivalent sampling rate as well as much higher operating frequency. With real time sampling and equivalent time sampling, the embedded system could perform rapid and effective measurement for the periodic signal which is either fast-varying or slow-varying and also be able to take real time sampling to the single-shot signals.

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

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

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

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

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