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The development of emerging information technologies, such as the Internet of Things (IoT), edge computing, and blockchain, has triggered a significant increase in IoT application services and data volume. Ensuring satisfactory service quality for diverse IoT application services based on limited network resources has become an urgent issue. Generalized processor sharing (GPS), functioning as a central resource scheduling mechanism guiding differentiated services, stands as a key technology for implementing on-demand resource allocation. The performance prediction of GPS is a crucial step that aims to capture the actual allocated resources using various queue metrics. Some methods (mainly analytical methods) have attempted to establish upper and lower bounds or approximate solutions. Recently, artificial intelligence (AI) methods, such as deep learning, have been designed to assess performance under self-similar traffic. However, the proposed methods in the literature have been developed for specific traffic scenarios with predefined constraints, thus limiting their real-world applicability. Furthermore, the absence of a benchmark in the literature leads to an unfair performance prediction comparison. To address the drawbacks in the literature, an AI-enabled performance benchmark with comprehensive traffic-oriented experiments showcasing the performance of existing methods is presented. Specifically, three types of methods are employed: traditional approximate analytical methods, traditional machine learning-based methods, and deep learning-based methods. Following that, various traffic flows with different settings are collected, and intricate experimental analyses at both the feature and method levels under different traffic conditions are conducted. Finally, insights from the experimental analysis that may be beneficial for the future performance prediction of GPS are derived.

With the rapid development of automatic vehicles (AVs), vehicles have become important intelligent objects in Smart City. Vehicles bring huge amounts of data for Intelligent Transportation System (ITS), and at the same time, they also put forward new application requirements. However, it is difficult to obtain and analyze massive data and provide accurate application services for AVs. In today's society of traffic explosion, how to plan the route of vehicles has become a hot issue. In order to solve this problem, we introduced content-data-friendly information-center networking (ICN) architecture into the Internet of Vehicles (IoV), and achieved efficient route planning for AVs through the Big Data acquisition and analysis architecture in ICN. We use the analytical capabilities of the network to achieve active cognitive access to traffic data. At the same time, we use game theory to achieve the incentive mechanism for task distribution and information sharing. Finally, the simulation results show that the method is effective.

The recent developments in the areas of the Internet of Things (IoTs) have provided a rapid growth in the epoch of the novel information-centric collections (IC-IoTs). In the IC-IoTs, expanding the ranges of information collections and reducing the costs are important issues for the information required platform. In the previous scenarios, the information is collected in a random manner, which leads to lower coverages and higher costs. Thus, a “optimizing the coverage via the unmanned aerial vehicles (UAVs) with lower costs for information-centric Internet of Things” (OCLC-IoTs) approach is established, which targets to improve the coverage ratio and to reduce the costs of the information-required platform via the cooperation of the information collectors and the UAVs. First, to improve the coverage ratio, an intensive strategy is proposed to inspire the information collectors to bid for the tasks published by the platform and an improved rolling horizon strategy (IRHS) strategy is designed to plan the flying routes of the UAVs to reach more coverage ranges. Then, to reduce the costs factor, the IRHS strategy is designed to shorten the flying routes of the UAVs under the prerequisite of guaranteeing coverage ratio to achieve fewers costs. Finally, a comprehensive theoretical analysis and experiments are provided, which indicates the advancements of the OCLC-IoTs scheme. Compared with the previous studies, the OCLC-IoTs scheme can improve the coverage ratio of information by 21.42% approximately and can reduce the cost ratio by 13.335% to 34.32%.

Big data processing includes both data management and data analytics. The data management step requires efficient cleaning, knowledge extraction, and integration and aggregation methods, whereas Internet-of-Multimedia-Things (IoMT) analysis is based on knowledge modeling and interpretation, which is more often performed by exploiting deep learning architectures. In the past couple of years, merging conventional and deep learning methodologies has exhibited great promise in ingesting multimedia big data, exploring the paradigm of transfer learning, association rule mining, and predictive analytics etc.

Unmanned aerial vehicles (UAVs) are frequently adopted in disaster management. The vision they provide is extremely valuable for rescuers. However, they face severe problems in their stability in actual disaster scenarios, as the images captured by the on-board sensors cannot consistently give enough information for deep learning models to make accurate decisions. In many cases, UAVs have to capture multiple images from different views to output final recognition results. In this paper, we desire to formulate the fly path task for UAVs, considering the actual perception needs. A convolutional neural networks (CNNs) model is proposed to detect and localize the objects, such as the buildings, as well as an optimization method to find the optimal flying path to accurately recognize as many objects as possible with a minimum time cost. The simulation results demonstrate that the proposed method is effective and efficient, and can address the actual scene understanding and path planning problems for UAVs in the real world well.

Millions of sensors are deployed to monitor the smart grid. They consume huge amounts of energy in the communication infrastructure. Therefore, the establishment of an energy-efficient medium access control (MAC) protocol for sensor nodes is challenging and urgently needed. The Quorum-based MAC protocol independently and adaptively schedules nodes' wake-up times and decreases idle listening and collisions, thereby increasing the network throughput and extending the network lifetime. A novel Quorum time slot adaptive condensing (QTSAC)-based MAC protocol is proposed for achieving delay minimization and energy efficiency for the wireless sensor networks (WSNs). Compared to previous protocols, the QTSAC-based MAC protocol has two main novelties: 1) It selects more Quorum time slots (QTSs) than previous protocols in the area that is far from the sink according to the energy consumption in WSNs to decrease the network latency and 2) It allocates QTSs only when data are transmitted to further decrease the network latency. Theoretical analyses and experimental results indicate that the QTSAS protocol can greatly improve network performance compared with existing Quorum-based MAC protocols. For intermediate-scale wireless sensor networks, the method that is proposed in this paper can enhance the energy efficiency by 24.64%-82.75%, prolong the network lifetime by 58%-27.31%, and lower the network latency by 3.59%-29.23%.

With the explosive growth of the Internet of Things, the gap between the rapidly growing demands of data rates and the existing bandwidth-limited network infrastructures has become increasingly prominent, leading to network congestion, high latency, and energy consumption and deterioration of the user's quality of service. To narrow this gap, an orchestrating data as services-based computing and communication (ODAS-CC) model is proposed to reduce the data rate, latency, and energy consumption for information-centric Internet of Things. The main innovations of the ODAS-CC model that differ from previous strategies are as follows. First, the services-based network architecture proposed in this paper can run on the current network effectively. In the proposed architecture, the data are orchestrated to services when they route to the data center; therefore, the data are transmitted after the service conversion, thereby forming a service-based network, which can greatly reduce the amount of data transmitted in the network, latency, and energy consumption. Second, a service conversion is performed in the process of routing such that the user's service request can be satisfied locally, thereby improving the user's quality of experience. Third, based on the ODAS-CC model, the performance evaluation model for energy consumption, latency, and load is provided in detail, and the performance of the ODAS-CC model is evaluated comprehensively. The theoretical analyses and experimental results show that compared with the previous approach, the ODAS-CC model can reduce the network traffic, the average latency of data upload by 26.3%, the service latency by up to 30.1%, and the data center load, thereby reducing energy consumption from 20.0% to 30.0%.

In this paper, we present the design and analysis of an energy-efficient 163-b elliptic curve cryptographic (ECC) processor suitable for passive ultrahigh frequency (UHF) radio frequency identification (RFID) tags that are usable for banknote authentication and anti-counterfeiting. Even partial public key cryptographic functionality has long been thought to consume too much power and to be too slow to be usable in passive UHF RFID systems. Utilizing a low-power design strategy with optimized register file management and an architecture based on the López-Dahab Algorithm, we designed a low-power ECC processor that is used with a modified ECC-DH authentication protocol. The ECC-DH authentication protocol is compatible with the ISO/IEC 18000-63 (“Gen2”) passive UHF RFID protocol. The ECC processor requires 12 145 gate equivalents. The ECC processor consumes 5.04 nJ/b at a frequency of 960 kHz when implemented in a 0.13-μm standard CMOS process. The tag identity authentication function requires 30 600 cycles to complete all scalar multiplication operations. This size, speed, and power of the ECC processor makes it practical to use within a passive UHF RFID tag and achieve up to 1500 banknote authentications per minute, which is sufficient for use in the fastest banknote counting machines.

In smart cities, wireless sensor networks (WSNs) act as a type of core infrastructure that collects data from the city to implement smart services. The security of WSNs is one of the key issues of smart cities. In resource-restrained WSNs, dynamic ongoing or unknown attacks usually steer clear of isolated defense components. Therefore, to resolve this problem, we propose a hierarchical framework based on chance discovery and usage control (UCON) technologies to improve the security of WSNs while still taking the low-complexity and high security requirements of WSNs into account. The features of continuous decision and dynamic attributes in UCON can address ongoing attacks using advanced persistent threat detection. In addition, we use a dynamic adaptive chance discovery mechanism to detect unknown attacks. To design and implement a system using the mechanism described above, a unified framework is proposed in which low-level attack detection with simple rules is performed in sensors, and high-level attack detection with complex rules is performed in sinks and at the base station. Moreover, software-defined networking and network function virtualization technologies are used to perform attack mitigation when either low-level or high-level attacks are detected. An experiment was performed to acquire an attack data set for evaluation. Then, a simulation was created to evaluate the resource consumption and attack detection rate. The results demonstrate the feasibility and efficiency of the proposed scheme.

Energy efficiency (EE) is critical to fully achieve the huge potentials of device-to-device (D2D) communications with limited battery capacity. In this paper, we consider the two-stage EE optimization problem, which consists of a joint spectrum and power allocation problem in the first stage, and a context-aware D2D peer selection problem in the second stage. We provide a general tractable framework for solving the combinatorial problem, which is NP-hard due to the binary and continuous optimization variables. In each stage, user equipments (UEs) from two finite and disjoint sets are matched in a two-sided stable way based on the mutual preferences. First, the preferences of UEs are defined as the maximum achievable EE. An iterative power allocation algorithm is proposed to optimize EE under a specific match, which is developed by exploiting nonlinear fractional programming and Lagrange dual decomposition. Second, we propose an iterative matching algorithm, which first produces a stable match based on the fixed preferences, and then dynamically updates the preferences according to the latest matching results in each iteration. Finally, the properties of the proposed algorithm, including stability, optimality, complexity, and scalability, are analyzed in detail. Numerical results validate the efficiency and superiority of the proposed algorithm under various simulation scenarios.

Nowadays, renewable energy sources in a micro-grid (MG) system have increased challenges in terms of the irregularly and fluctuation of the photovoltaic and wind turbine units. It is necessary to develop battery energy storage. The MG central controller is helping to develop it in the MG system for improving the time of availability. Thus, reducing the total energy expenses of MG and improving the renewable energy sources (battery energy storage) are considered together with the operation management of the MG system. This study proposes fitness-based modified game particle swarm optimisation (FMGPSO) algorithm to optimise the total costs of operation and pollutant emissions in the MG and multi-MG system. The optimal size of battery energy storage is also considered. A non-dominated sorting genetic algorithm-III, a multi-objective covariance matrix adaptation evolution strategy, and a speed-constrained multi-objective particle swarm optimisation are compared with the proposed FMGPSO to show the performance. The results of the simulation show that the FMGPSO outperforms both the comparison algorithms for the minimisation operation management problem of the MG and the multi-MG system.

Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP) is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME) is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.

In this paper, a Per-Hop Acknowledgement (PHACK)-based scheme is proposed for each packet transmission to detect selective forwarding attacks. In our scheme, the sink and each node along the forwarding path generate an acknowledgement (ACK) message for each received packet to confirm the normal packet transmission. The scheme, in which each ACK is returned to the source node along a different routing path, can significantly increase the resilience against attacks because it prevents an attacker from compromising nodes in the return routing path, which can otherwise interrupt the return of nodes’ ACK packets. For this case, the PHACK scheme also has better potential to detect abnormal packet loss and identify suspect nodes as well as better resilience against attacks. Another pivotal issue is the network lifetime of the PHACK scheme, as it generates more acknowledgements than previous ACK-based schemes. We demonstrate that the network lifetime of the PHACK scheme is not lower than that of other ACK-based schemes because the scheme just increases the energy consumption in non-hotspot areas and does not increase the energy consumption in hotspot areas. Moreover, the PHACK scheme greatly simplifies the protocol and is easy to implement. Both theoretical and simulation results are given to demonstrate the effectiveness of the proposed scheme in terms of high detection probability and the ability to identify suspect nodes.

Wireless sensor networks (WSNs) are widely applied in various industrial applications. In this paper, we present an efficient data gathering scheme that guarantees the Quality of Service and optimizes the following network performance metrics as well as the end-to-end reliability in WSNs: (1) minimum total energy consumption; (2) minimum unit data transmitting energy consumption; and (3) maximum utilization efficiency defined as network lifetime per unit deployment. We first transform the performance optimization problem into a problem to optimize the following parameters: (1) deployed nodal number N*; (2) nodal placement d*; and (3) nodal transmission structure p*. Then, we prove that the optimization problem is solvable mathematically. For our observation, the sensor nodes close to the sink trend failed early since they consumed more energy with heavier relay traffic destined for the sink, which seriously affects the network performance. The key point of this optimization is adopting lower reliability requirements and shorter transmission distance for nodes near the sink. Consequently, this reduces the energy consumption of the nodes in the hotspot area. Meanwhile, it adopts higher reliability requirements and farther transmission distance for nodes far from the sink to make full use of the node residual energy, so as to optimize the network performance without harming network reliability. Numerical simulation results demonstrate that our optimal approach improves the network lifetime by 18%-48% and network utility by 17%, and guarantees desire reliability level.

With the explosive growth of mobile data traffic and rapidly rising energy price, how to implement caching at small cells in an energy-efficient way is still an open problem and requires further research efforts. In this paper, we study the energy-efficient context-aware resource allocation problem, which falls into the category of mixed integer nonlinear programming (MINLP) and is NP-hard. To provide a tractable solution, the MINLP problem is decoupled and reformulated as a one-to-one matching problem under two-sided preferences, which are modeled as the maximum energy efficiency that can be achieved under the expected matching. An iterative algorithm is developed to establish preference profiles by employing nonlinear fractional programming and Lagrange dual decomposition. Then, we propose an energy-efficient matching algorithm based on the Gale-Shapley algorithm, and provide the detailed discussion and analysis of stability, optimality, implementation issues, and algorithmic complexity. The proposed matching algorithm is also extended to scenarios with preference, indifference, and incomplete preference lists by introducing some tie-breaking and preference deletion rules. The simulation results demonstrate that the proposed algorithm achieves significant performance and satisfaction gains compared with the conventional algorithms.

In an industrial system, wireless sensor networks (WSNs) are usually adapted to industrial applications. Industrial system is a novel scenario to apply WSNs. Industrial WSNs are the base to establish a supervisory control and data acquisition system with the benefits of extending the network boundaries and enhancing the network scalability of the WSNs. The integration of industrial systems, such as smart grids and social networks, is an important trend for new network technologies. In many application scenarios of industrial systems, WSNs are controlled by different authorities. The network nodes that belong to different domains can share the sensor data by standard protocols. Moreover, in an applications, scenario that has high security requirements, the nodes of social networking WSNs could belong to different security levels; thus, these data can be controlled only by specific types of users. Therefore, the cross-domain fine-grained data usage is the core problem for this approach. To address this problem, this paper focuses on the cross-domain fine-grained data usage control mechanism of social networking WSNs in industrial systems, which includes cross-domain fine-grained access control and fuzzy clustering for sensing data for efficient analysis. In addition, dynamic service composition is proposed for data usage. The simulation results verify the feasibility and data usage effectiveness of the proposed scheme.

Wireless sensor networks (WSNs) have been proliferating due to their wide applications in both military and commercial use. However, one critical challenge to WSNs implementation is source location privacy. In this paper, we propose a novel tree-based diversionary routing scheme for preserving source location privacy using hide and seek strategy to create diversionary or decoy routes along the path to the sink from the real source, where the end of each diversionary route is a decoy (fake source node), which periodically emits fake events. Meanwhile, the proposed scheme is able to maximize the network lifetime of WSNs. The main idea is that the lifetime of WSNs depends on the nodes with high energy consumption or hotspot, and then the proposed scheme minimizes energy consumption in hotspot and creates redundancy diversionary routes in nonhotspot regions with abundant energy. Hence, it achieves not only privacy preservation, but also network lifetime maximization. Furthermore, we systematically analyze the energy consumption in WSNs, and provide guidance on the number of diversionary routes, which can be created in different regions away from the sink. In addition, we identify a novel attack against phantom routing, which is widely used for source location privacy preservation, namely, direction-oriented attack. We also perform a comprehensive analysis on how the direction-oriented attack can be defeated by the proposed scheme. Theoretical and experimental results show that our scheme is very effective to improve the privacy protection while maximizing the network lifetime.

Vehicular ad hoc networks support a wide range of promising applications including vehicular sensing networks, which enable vehicles to cooperatively collect and transmit the aggregated traffic data for the purpose of traffic monitoring. The reported literatures mainly focus on how to achieve the data aggregation in dynamic vehicular environment, while the security issue especially on the authenticity and integrity of aggregation results receives less attention. In this study, we introduce a basic aggregation scheme which could aggregate the data and the message authentication codes by using syntactic aggregation and cryptographic aggregation . To tolerate duplicate messages and further improve the aggregation performance, we introduce a secure probabilistic data aggregation scheme based on Flajolet-Martin sketch and sketch proof technique. We also discuss the tradeoff between the bandwidth efficiency and the estimation accuracy. Extensive simulations and analysis demonstrate the efficiency and effectiveness of the proposed scheme.

Key management techniques for secure wireless-sensor-networks-based applications must minimally incorporate confidentiality, authenticity, integrity, scalability, and flexibility. Signcryption is the proper primitive to do this. However, existing signcryption schemes are heavyweight and not suitable for resource-limited sensors. In this paper, we at first propose a braid-based signcryption scheme and then develop a key establishment protocol for wireless sensor networks. From the complexity view, our proposal is 2 15 times faster than RSA-based ones. As far as we know, our proposal is the first signcryption scheme based on noncommutative algebraic structures.