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A Flying Ad Hoc Network (FANET) is a self-organizing wireless network comprised of clusters of Unmanned Aerial Vehicles (UAVs) or drones that communicate while nearby. FANETs are increasingly used in a variety of applications, including smart ports, delivery of products, construction, monitoring of the environment and climate, and military surveillance. FANETs research is being driven by the potential for UAVs to be utilized in these regions. The purpose of this paper is to provide a comprehensive analysis of the most important FANET characteristics, mobility models, applications, and routing protocols. The present paper is an effort to provide a comprehensive description of the various routing techniques utilized by the most prevalent routing protocols in FANETs, including topology-based, position- based, hierarchical, swarm-based, and Delay Tolerant Networking (DTN) protocols. Reinforcement learning and deep reinforcement learning are both encompassed in a newly anticipated classification. In the meanwhile, this study primarily centres around the taxonomy for learning agents (single- agent, multi-agent) and learning models (model-based and free-model). In addition, the paper intends to shed light on identifying the applications of FANETs in various categories and identify research gaps and future opportunities in this field. In addition, it compares the results qualitatively to those of the previous surveys. Any future work on the FANET routing protocol could benefit from this paper as a reference and roadmap.

Abstract With the refinement and development of the Vehicle to Everything (V2X) concept, its security issues have gradually come to the fore, revealing many security risks and increasing security requirements for V2X, and many protective measures have likewise emerged. The article first introduces the development history of the past Internet of Vehicles(IoV), summarizes some common V2X security threats, surveys the security technologies used for V2X communication, and outlines the development of each technology and the proposed security protocols in the last 3 years. Due to the different advantages and disadvantages of previous protection schemes, the idea of using National Cryptography to supplement the security scheme or designing a new security scheme article based on the National Cryptography Algorithms((AKA SM algorithms) is proposed. The survey then introduces the SM2, SM3, SM9, and ZUC algorithms, describes the development and application of the SM commercial algorithm in recent years, and finally, statistics and introduces the part of the development process of the security protocols currently used in IoV regarding the SM algorithms and gives some application prospects.

Aiming at the data island problem caused by the imperativeness of confidentiality of sensitive information, a secure and collaborative intrusion detection system (CIDS) for industrial cyber physical systems (CPS) was proposed, called PFD-GAN.Specifically, a novel semi-supervised intrusion detection model was firstly developed by improving external classifier-generative adversarial network (EC-GAN) with Wasserstein distance and label condition, to strengthen the classification performance through the use of synthetic data.Furthermore, local differential privacy (LDP) technology was incorporated into the training process of developed EC-GAN to prevent sensitive information leakage and ensure privacy and security in collaboration.Moreover, a decentralized federated distillation (DFD)-based collaboration was designed, allowing multiple industrial CPS to collectively build a comprehensive intrusion detection system (IDS) to recognize the threats under the entire cyber systems without sharing a uniform template model.Experimental evaluation and theory analysis demonstrate that the proposed PFD-GAN is secure from the threats of privacy leaking and highly effective in detecting various types of attacks on industrial CPS.

The implementation of Open Radio Access Network (O-RAN) architecture in Internet of Things (IoT) systems has garnered significant attention as a means to fulfill the stringent requirements of ultra-low latency and ultra-low energy consumption in future IoT systems. Although the traditional edge network architecture has been extensively employed, it continues to pose challenges in terms of synchronizing the integration of global and local information for the design of an optimal offloading strategy in edge servers, thus hindering the reduction of latency and energy consumption in IoT devices. In an effort to decrease the latency and energy consumption of IoT devices (IoTDs), we propose a computation offloading problem and employs the Successive Convex Approximation (SCA) algorithm to convert the non-convex problem into a convex problem. The proposed strategy aims to minimize the energy consumption of IoTDs while ensuring Quality of Service (QoS) requirements. The results of the experiment demonstrate that the average energy consumption of terminal devices can be reduced by 20%. Additionally, this strategy is found to be more effective in reducing IoTDs' latency and energy consumption as compared to the traditional edge network architecture.

Vehicle Ad-hoc Networks (VANETs) have experienced significant development in recent years, playing a crucial role in enhancing the driving experience by enabling safer and more efficient inter-vehicle interactions through information exchange. Vehicle-to-Vehicle (V2V) communication is particularly vital as it not only helps to prevent collisions and improve traffic efficiency but also provides essential situational awareness to drivers or autonomous driving systems. Communication is typically supported by roadside units (RSUs); however, in practical applications, vehicles may exceed the communication range of RSUs, thus exposing them to various malicious attacks. Additionally, considering the limited computational resources of onboard units (OBUs) in vehicles, there is a high demand for designing lightweight security protocols that support V2V communication. To address this issue, this paper proposes an efficient anonymous V2V identity-authentication protocol tailored for scenarios that lack RSU support. The proposed protocol was formally assessed using the Scyther tool, demonstrating its capability to withstand major typical malicious attacks. Performance evaluations indicate that the proposed protocol is efficient in terms of communication and computational overhead, making it a viable solution for V2V communication.

Named Data Networking (NDN) is a promising network architecture that differs from the traditional TCP/IP network, as it focuses on data rather than the host. A new secure model is required to provide the data-oriented trust instead of the host-oriented trust. This paper proposes a new secure solution in the NDNs named Secure Mechanism supported by Certificateless Digital Signature and Blockchain (CLDS-B). The CLDS-B scheme employs a certificateless digital signature to guarantee the authentication and integrity of data. On the one hand, the key escrow problem has been solved to eliminate the risks of compromised private key generators; on the other hand, the data name has been bound to the public key to prevent the false public key. Moreover, the blockchain is used to manage cryptographic information. Each domain designates an information service entity to join the blockchain so that the consumer could retrieve the cryptographic information public parameter in the local domain if necessary. Furthermore, due to the decentralization of the blockchain, the CLDS-B would be robust to resist the single-node failure. Simulation results show that the CLDS-B scheme outperforms a classic NDN scheme, although it shows slightly inferior to the other secure NDN scheme. The security verification and analysis show that the CLDS-B would resist the key escrow attack. The CLDS-B would be a competitive solution in scenarios with a high-security level.

In the realm of the fifth-generation (5G) wireless cellular networks, renowned for their dense connectivity, there lies a substantial facilitation of a myriad of Internet of Things (IoT) applications, which can be supported by the massive machine-type communication (MTC) technique, a fundamental communication framework. In some scenarios, a large number of machine-type communication devices (MTCD) may simultaneously enter the communication coverage of a target base station. However, the current handover mechanism specified by the 3rd Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead within the access and core networks, which may have negative impacts on network efficiency. Additionally, other existing solutions are vulnerable to malicious attacks such as Denial of Service (DoS), Distributed Denial of Service (DDoS) attacks, and the failure of Key Forward Secrecy (KFS). To address this challenge, this paper proposes an efficient and secure handover authentication protocol for a group of MTCDs supported by blockchain technology. This protocol leverages the decentralized nature of blockchain technology and combines it with certificateless aggregate signatures to mutually authenticate the identity of a base station and a group of MTCDs. This approach can reduce signaling overhead and avoid key escrow while significantly lowering the risk associated with single points of failure. Additionally, the protocol protects device anonymity by encrypting device identities with temporary anonymous identity markers with the Elliptic Curve Diffie–Hellman (ECDH) to abandon serial numbers to prevent linkage attacks. The resilience of the proposed protocol against predominant malicious attacks has been rigorously validated through the application of the BAN logic and Scyther tool, underscoring its robust security attributes. Furthermore, compared to the existing solutions, the proposed protocol significantly reduces the authentication cost for a group of MTCDs during handover, while ensuring security, demonstrating commendable efficiency.

In order to solve the shortcomings of the existing Beidou user equipment access solutions such as prolonged authentication, and in response to the need of user privacy protection, a user equipment access authentication protocol based on location key was proposed, which realized the mutual authentication and session key negotiation between entities.Combining Beidou’s location information and master key to form a two-factor, the proposed protocol solved the security problem of the master key leaking due to the equipment being captured during the authentication process to some extent.The proposed protocol can meet different security requirements through the verification of the Scyther formal tool.Compared with other similar schemes, it has better performance overhead, and is suitable for Beidou satellite navigation system with limited node resources.

Federated Learning (FL), as an emerging paradigm in distributed machine learning, has received extensive research attention. However, few works consider the impact of device mobility on the learning efficiency of FL. In fact, it is detrimental to the training result if heterogeneous clients undergo migration or are in an offline state during the global aggregation process. To address this issue, the Optimal Global Aggregation strategy (OGAs) is proposed. The OGAs first models the interaction between clients and servers of the FL as a Markov Decision Process (MDP) model, which jointly considers device mobility and data heterogeneity to determine local participants that are conducive to global aggregation. To obtain the optimal client participation strategy, an improved σ-value iteration method is utilized to solve the MDP, ensuring that the number of participating clients is maintained within an optimal interval in each global round. Furthermore, the Principal Component Analysis (PCA) is used to reduce the dimensionality of the original features to deal with the complex state space in the MDP. The experimental results demonstrate that, compared with other existing aggregation strategies, the OGAs has the faster convergence speed and the higher training accuracy, which significantly improves the learning efficiency of the FL.

The rapid growth of edge devices and mobile applications has driven the adoption of edge computing to handle computing tasks closer to end-users. However, the heterogeneity of edge devices and their limited computing resources raise challenges in the efficient allocation of computing resources to complete services with different characteristics and preferences. In this paper, we delve into an edge scenario comprising multiple Edge Computing Servers (ECSs), multiple Device-to-Device (D2D) Edge Nodes (ENs), and multiple edge devices. In order to address the resource allocation challenge among ECSs, ENs, and edge devices in high-workload environments, as well as the pricing of edge resources within the resource market framework, we propose a Risk Assessment Contract Algorithm (RACA) based on risk assessment theory. The RACA enables ECSs to assess risks associated with local users by estimating their future revenue potential and updating the contract autonomously at present and in the future. ENs acquire additional resources from ECSs to efficiently complete local users’ tasks. Simultaneously, ENs can also negotiate reasonable resource requests and pricing with ECSs by a Stackelberg game algorithm. Furthermore, we prove the unique existence of Nash equilibrium in the established game, implying that equilibrium solutions can stably converge through computational methods in heterogeneous environments. Finally, through simulation experiments on the dataset, we demonstrate that risk assessment can better enhance the overall profit capability of the system. Moreover, through multiple experiments, we showcase the stability of the contract’s autonomous update capability. The RACA exhibits better utility in terms of system profit capabilities, stability in high-workload environments, and energy consumption. This work provides a more dynamic and effective solution to the resource allocation problem in edge systems under high-workload environments.

With the increasing demand for a digital world, the Industrial Internet of Things (IIoT) is growing rapidly across various industries. In manufacturing, particularly in Industry 4.0, the IIoT assumes a vital role. It encompasses many devices such as sensing devices, application servers, users, and authentication servers within workshop settings. The security of the IIoT is a critical issue due to wireless networks’ open and dynamic nature. Therefore, designing secure protocols among those devices is an essential aspect of IIoT security functionality and poses a significant challenge to the IIoT systems. In this paper, we propose a lightweight anonymous authentication protocol to preserve privacy for IIoT users, enabling secure IIoT communication. The protocol has been validated to demonstrate its comprehensive ability to overcome various vulnerabilities and prevent malicious attacks. Finally, the performance evaluation confirms that the proposed protocol is more effective and efficient than the existing alternatives.

Urban computing utilizes unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods to create win-win-win solutions which intelligently improve people’s lives, urban environments, and city operation systems. With the help of cloud computing, the Internet of Things, device-to-device (D2D) communication, artificial intelligence (AI), big data, and urban computing and intelligence will bridge the gap of ubiquitous sensing, intelligent computing, cooperative communication, and mass data management technologies, to create novel solutions that improve urban environments, human life quality, and smart city systems. Thus, urban computing and intelligence has recently attracted significant attention from industry and academia for building smart cities.

The advent of Fifth Generation (5G) and the Internet of Things (IoT) is expected not only to make it possible to collect and disseminate information for various crowd-sensing services in densely populated environments but also to put forward a higher request on these services with the rapid evolution of artificial intelligence and edge computing, which provides cloud computing and cache capabilities to reduce the computational load of cellular networks, displacing it at the edges of such networks. However, the costs for deployment and maintenance of mobile edge computing (MEC) are still high. Human-driven edge computing (HEC) is a novel model which integrates the elements of human, devices, internet and information, and combines the power of MEC architecture and the large-scale sensing ability of mobile crowd-sensing (MCS). Realizing better data spreading and environmental coverage in smart cities based on HEC has aroused a great deal of research interest from academia and industry. Although the studies of human-driven edge computing for 5G and the IoT are attractive, there are many open research problems, such as fusion analysis, efficient resource usage, low latency communication, large-scale search, and data security and privacy.

Modern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies on secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as smart grids are vulnerable and subject to various cyberattacks. Various machine learning based classifiers were proposed for intrusion detection in smart grids. However, each of them has respective advantage and disadvantages. Aiming to improve the performance of existing machine learning based classifiers, this paper proposes an adaptive deep learning algorithm with a data pre-processing module, a neural network pre-training module and a classifier module, which work together classify intrusion data types using their high-dimensional data features. The proposed Adaptive Deep Learning (ADL) algorithm obtains the number of layers and the number of neurons per layer by determining the characteristic dimension of the network traffic. With transfer learning, the proposed ADL algorithm can extract the original data dimensions and obtain new abstract features. By combining deep learning models with traditional machine learning-based classification models, the performance of classification of network traffic data is significantly improved. By using the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset, experimental results show that the proposed ADL algorithm improves the effectiveness of existing intrusion detection methods and reduces the training time, indicating a promising candidate to enhance network security in smart grids.

The capacity of highways has been an ever-present constraint in the 21st century, bringing about the issue of safety with greater likelihoods of traffic accidents occurring. Furthermore, recent global oil prices have inflated to record levels. A potential solution lies in vehicular platooning, which has been garnering attention, but its deployment is uncommon due to cyber security concerns. One particular concern is a Sybil attack, by which the admission of fake virtual vehicles into the platoon allows malicious actors to wreak havoc on the platoon itself. In this paper, we propose a secure management scheme for platoons that can protect major events that occur in the platoon operations against Sybil attacks. Both vehicle identity and message exchanged are authenticated by adopting key exchange, digital signature and encryption schemes based on elliptic curve cryptography (ECC). Noteworthy features of the scheme include providing perfect forward secrecy and both group forward and backward secrecy to preserve the privacy of vehicles and platoons. Typical malicious attacks such as replay and man-in-the-middle attacks for example can also be resisted. A formal evaluation of the security functionality of the scheme by the Canetti–Krawczyk (CK) adversary and the random oracle model as well as a brief computational verification by CryptoVerif were conducted. Finally, the performance of the proposed scheme was evaluated to show its time and space efficiency.

DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In this paper, BP (back propagation) neural networks and game theory are introduced to design detection methods and defense mechanisms for the DoS attacks. The BP neural network DoS attacks detection model uses KDDCUP99 as the dataset and selects multiple feature vectors from the dataset that can efficiently identify DoS attacks by large-scale training, which improves the accuracy of detecting DoS attacks to 99.977%. Furthermore, we use game theory to perform secondary analysis on DoS attacks that are not recognized by the neural network model, so that the detection rate of Dos attacks increases from 99.97% to 99.998%. Finally, we propose a DoS attacks defense strategy based on game theory. The simulation results show that the proposed detection method and defense strategy are effective for DoS attacks.

Underwater acoustic sensor network (UASN) has become one of the enabling technologies for the development of future ocean observation systems (OOSs). However, the UASN could be severely damaged because of the harsh environment that causes the simultaneous failure of many sensor nodes, thus leading to the partition of the network into multiple disconnected segments. In this paper, we studied the crucial issue to reestablish the network connectivity with the least quantity of employed relay nodes. To achieve a clear understanding of the issue, we present its integer nonlinear programming formulation, which is generally NP-hard. So, with the aim of solving the problem efficiently, an original heuristic scheme is proposed in this paper. Two fundamental algorithms are integrated into the scheme, namely, alternating tree construction and Fermat-point selection (ATCFS) as a whole. The results of extensive simulation experiment have confirmed that the ATCFS can solve this problem simply and effectively.

Due to the dynamic in nature, the vulnerabilities that exist in VANET are much higher when compared with that of the wired network infrastructure. In DoS attacks, the legitimate users are prohibited from accessing the services or network resource. The primary goal of the attack to make the desired destination vehicle unavailable or relegate the message all the way through the network affects the reachability. The proposed reCAPTCHA controller mechanism prevents the automated attacks similarly like botnet zombies. The reCAPTCHA controller is used to check and prohibit most of the automated DDoS attacks. For implementing this technique, the information theory based metric is used to analyze the deviation in users request in terms of entropy. Frequency and entropy are the metrics used to measure the vulnerability of the attack. The stochastic model based reCAPTCHA controller is used as a prevention mechanism for the large botnet based attackers. To inspect the efficiency of the proposed method, various network parameters are considered such as Packet Delivery Ratio (PDR), Average Latency (AL), Detection Rate (DR) and Energy Consumption (EC). In the proposed research work, the metric PDR is used to know successful delivery of data packets to the destination vehicle without any interrruption. These parameters are used to measure how effectively the data is delivered to the destination from source vehicle.

It is essential for Vehicular Ad-hoc networks (VANETs) to have reliable vehicles for communication with vehicles. VANET is dynamical network where the vehicles frequently alter their place. Safe routing is of great essence at the time of routing process to fit in shared trust/belief involving these nodes. Occasionally, the malicious node transmits the counterfeit data amid other nodes. To find out trust/belief is deemed to be a difficult task when the malicious nodes try to distort the discovery of route or transmission of data within the network. Researchers have worked extensively to ensure a safe routing process with trust-oriented applications. We develop the framework based on trust with a fresh mechanism to determine DDoS attacks in VANET. The major trust elements in the evaluation of trust are frequency value statistics, trust hypothesis statistics, residual energy, trust policy, and data factor. Based on the trust elements, the generation of trust evaluation matrix takes place. We develop the suggested trust mechanism in an innovative manner to offer the security in a better manner by avoiding the trespassers in the network. The deterrence design by trust evaluation mechanism in combination with a clustering method is proficiently made use for the identification of the attacker and reduction of the price concerning detection method. The suggested system optimizes the utilization of a bandwidth without compromising the security of the nodes in the network.

The wireless technology has revolutionized and had a significant impact on every aspect of people's life. Confidential information, financial transactions, and sensitive conversations are frequent via the wireless network and securing all these data are of the utmost importance. In this paper, we discuss the major weaknesses of the long-term evolution (LTE) authentication process and propose a new approach-the hybrid evolved packet system (HEPS) protocol to address the vulnerabilities. The proposed protocol has been verified logically, using Burrows-Abadi-Needham logic, and systematically, using the automated validation of internet security protocol and application tool. The HEPS protocol will optimize the performance of the LTE authentication process and fundamentally solve the security issue of the process.

Wireless radio frequency identification (RFID) has been widely used as the core technology of the Internet of Things, but it also brings many potential security risks. In this paper, two typical RFID security authentication protocols are analyzed in depth. The results show that the two protocols have security risks, and two targeted attack methods are proposed. Next, two RFID security authentication protocols which can resist the DoS, replay, and tracing attacks are designed. The first protocol uses index grouping and dynamic renewal mechanism. Since the Rabin public key encryption algorithm verification only requires square and modulo operations, the second protocol introduces the public key encryption algorithm into the cost-sensitive RFID tag, and formally verifies the functionality of the proposed scheme by SPIN. Furthermore, the security of the protocol is proven by the BAN logic. The grouping index method can effectively improve the ability to locate tags in a large database and enhance the practicability of the protocol. The introduction of the Rabin public key algorithm improves the security of the protocol under the condition of cost control.

The futuristic fifth-generation cellular network (5G) not only supports high-speed internet, but must also connect a multitude of devices simultaneously without compromising network security. To ensure the security of the network, the Third Generation Partnership Project (3GPP) has standardized the 5G Authentication and Key Agreement (AKA) protocol for mutually authenticating user equipment (UE), base stations, and the core network. However, it has been found that 5G-AKA is vulnerable to many attacks, including linkability attacks, denial-of-service (DoS) attacks, and distributed denial-of-service (DDoS) attacks. To address these security issues and improve the robustness of the 5G network, in this paper, we introduce the Secure Blockchain-based Authentication and Key Agreement for 5G Networks (5GSBA). Using blockchain as a distributed database, our 5GSBA decentralizes authentication functions from a centralized server to all base stations. It can prevent single-point-of-failure and increase the difficulty of DDoS attacks. Moreover, to ensure the data in the blockchain cannot be used for device impersonation, our scheme employs the one-time secret hash function as the device secret key. Furthermore, our 5GSBA can protect device anonymity by mandating the encryption of device identities with Subscription Concealed Identifiers (SUCI). Linkability attacks are also prevented by deprecating the sequence number with Elliptic Curve Diffie–Hellman (ECDH). We use Burrows–Abadi–Needham (BAN) logic and the Scyther tool to formally verify our protocol. The security analysis shows that 5GSBA is superior to 5G-AKA in terms of perfect forward secrecy, device anonymity, and mutual Authentication and Key Agreement (AKA). Additionally, it effectively deters linkability attacks, replay attacks, and most importantly, DoS and DDoS attacks. Finally, the performance evaluation shows that 5GSBA is efficient for both UEs and base stations with reasonably low computational costs and energy consumption.

Data leakage is a growing insider threat in information security among organizations and individuals. A series of methods has been developed to address the problem of data leakage prevention (DLP). However, large amounts of unstructured data need to be tested in the big data era. As the volume of data grows dramatically and the forms of data become much complicated, it is a new challenge for DLP to deal with large amounts of transformed data. We propose an adaptive weighted graph walk model to solve this problem by mapping it to the dimension of weighted graphs. Our approach solves this problem in three steps. First, the adaptive weighted graphs are built to quantify the sensitivity of the tested data based on its context. Then, the improved label propagation is used to enhance the scalability for fresh data. Finally, a low-complexity score walk algorithm is proposed to determine the ultimate sensitivity. Experimental results show that the proposed method can detect leaks of transformed or fresh data fast and efficiently.

The recent research trend of extending cloud computing to vehicles by leveraging under-utilized on-board capabilities [also known as vehicular cloud (VC)] is unsurprising, partly due to the increasing popularity of intelligent vehicles (with on-board computing equipment) and cloud computing. However, VC deployment is still challenging: how to securely verify responding vehicles, how to recruit competent vehicles, and how to achieve privacy preservation, are among the remaining research challenges. In this paper, we propose a secure and privacy-preserving incentive mechanism under the vehicular advertisement dissemination setting, which enables vehicles to opportunistically perform on-demand advertisement dissemination tasks and (financially) benefit from the completed task. Specifically, the road side unit (RSU) representing the advertisement dissemination server first announces a dissemination task, and performs the privacy-preserving access control of the responding vehicles. Then the RSU selects the participating vehicles among a pool of verified competent vehicles, and acknowledges the participation of selected vehicles with a secure secret sharing scheme; meanwhile, the selected vehicles performs the dissemination task with incentives. Finally, we prove the security of the proposed scheme in terms of secure access control and privacy preservation, and demonstrate its efficiency via simulation results (i.e., improves the amount of advertisement dissemination and brings incentives to participating vehicles).

Localization plays a more and more important role in underwater wireless sensor networks (UWSNs). But in the large-scale UWSNs, the localization algorithm can’t be realized for the continuous packet collision. Therefore, we need to consider the impact of MAC protocol on the positioning algorithm. First, this paper proposes a multi-layer positioning model based on the underwater network architecture. Then according to the non-synchronous localization scheme, we analyze the reason of packet collisions and propose the variable interval ALOHA (VI-ALOHA) protocol based on the Poisson distribution. The VI-ALOHA protocol reduces the collision by adding random space–time. Through the comparison of localization coverage, packet loss, and localization time, this paper evaluates the impact on the localization result in equal interval ALOHA (EI-ALOHA) and VI-ALOHA. Our simulation results show that the MAC protocol has a significant influence on localization; besides, the localization coverage and the packet loss of VI-ALOHA protocol are above 20% better than the EI-ALOHA.

The software-defined network (SDN) is a new networking paradigm to improve network performance via logically centralized control and open standardized interfaces. However, the introduction of SDN technology faces many difficulties due to operational and economic constraints. Therefore, an incremental deployment scenario, the so-called hybrid SDN, is preferred. The transition from a legacy network to a pure SDN may take a long time; hence, it is of vital importance to consider the network optimization problem for hybrid SDNs. With SDN-enabled devices, multi-paths, where each flow can have multiple alternative paths, can be supported in bandwidth allocation. In contrast to traditional bandwidth allocation mechanisms in pure IP networks and full SDNs, this paper proposes a utility maximization model with the coexistence of single paths and multi-paths in the network, which aims to facilitate the transition to SDNs. We also design a novel algorithm to solve the maximization model, which is no longer strictly concave. Extensive simulations are conducted to evaluate the performance of the proposed bandwidth allocation strategy. Compared with existing strategies, our strategy has a good performance in terms of utility improvement. The results also show that with an increasing number of devices deployed in the SDN, more paths can be used to allocate the bandwidth; thus, a greater network utility can be achieved.

Energy efficiency in data center networks (DCNs) is critical to the operations of modern large-scale data centers. One effective way is to make the size of DCNs elastic along with flow demands by centralized routing and scheduling, i.e., turning off idle network components to reduce the power consumption. As such, software-defined networking (SDN) is widely used for achieving such elasticity conveniently. Meanwhile, the scale and structure of modern DCNs get much larger and more complex. Central control and global computing become impractical due to the heavy time and space complexity. Therefore, distributed power control is necessary for large-scale SDN-DCNs, and yet there are few research achievements in this area. In this paper, we present an extensible energy-efficient mechanism, which: 1) leverages distributed flow routing for both intraand inter-domain elephant flows and 2) extendedly considers distributed energy efficiency for control plane. A local power-saving function is operated within each domain of control plane, and a distributed energy-efficient routing algorithm is computed to optimize the effectiveness for the inter-domain flows. The simulation results demonstrate that this distributed mechanism applies to large-scale DCNs and achieves an effective power saving.

IP mutation is an effective moving target defense method against sniffer or hijacking attack. The mutation frequency is one of the most important parameters that influence the security of mutation method. However, higher frequency is inconsistent with data transmission that will decrease the efficiency and stability. Moreover, most of existing mutation methods have shortcomings under various conditions, such as address allocation or network architecture. In this paper, sliding window and full transparent (SWIFT) scheme for IPv6 address mutation is proposed. With the sliding window design, the SWIFT scheme can provide an address mutation with very high frequency. This scheme is transparent to both network side and user side so that the existing equipment and architecture need not to be changed. A prototype by the SWIFT scheme is designed and developed over an IPv6 network. The experiment result shows that our method can achieve high transmission efficiency with a high mutation frequency, which provides a good experience for most mutation methods.

The IEEE 802.22 standard regulates wireless regional area network (WRAN) operating at the very high frequency and ultrahigh frequency television white space (TVWS) bands. WRAN supports extensible authentication protocol (EAP)-based authentication schemes. However, due to its public key cryptography operations, a full EAP-based authentication scheme is time-consuming, which is unacceptable for the WRAN handover process. In this paper, an efficient EAP-based pre-authentication scheme for inter-WRAN handovers (EPW) in TVWS is proposed. By applying the proposed pre-authentication scheme, the customer premises equipment and the target secondary user base station can accomplish a seamless handover using symmetric key. Through logic derivation by Burrows, Abadi, and Needham logic and formal verification by automated validation of Internet security protocols and applications, we conclude that the proposed EPW scheme can obtain mutual authentication and maintain key secrecy with a high resistance to attack. Additionally, the performance of the EPW scheme has been investigated by simulation experiments. The results show that the EPW scheme provides a lower computation delay than that mandated by the security scheme regulation of the IEEE 802.22 standard.

With the recent rapid development of cloud computing technology, how to reduce the costs of a cloud data center effectively has become an important issue. The study on virtual machine deployment mainly aims at deploying virtual machine resources required by users on a physical server rationally and effectively. This paper proposes a multi-population ant colony algorithm to solve problems of virtual machine deployment. With resource wastage and energy consumption as optimization objectives, this algorithm uses multiple ant colonies for the solution and determines strategies for information exchange among ant colonies according to the information entropy of each population to guarantee the balance of its convergence and diversity. The simulation results show that this algorithm has better performance than the single-population ant colony algorithm and can reduce resource wastage and energy consumption effectively for high-demand virtual machine deployment.

Huge amounts of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in road sensor networks. Publish/subscribe system is one kind of important applications for analyzing and processing these huge mounts of data in road sensor networks, which is required to support millions of subscriptions and filter a message in milliseconds. Since the messages arrive continuously at a high speed, rapid processing of the messages is definitely a challenge. This paper mainly addresses the issue of parameterized spatio-textual publish/subscribe problem in road sensor networks. First, with considering both the network distance and textual similarity of the subscriptions and messages, the road network structure, together with the subscriptions and the messages will be partitioned and organized efficiently, and a combined index structure, called basic indexing architecture, is proposed. Second, several effective pruning techniques which consider both location information and textual information are presented to cut down the processing overhead. Moreover, by employing these pruning techniques into the basic indexing architecture, an more efficient index, called enhanced indexing architecture, is presented. Third, an efficient processing algorithm is designed to improve the scalability. Finally, extensive simulations are conducted to show the efficiency and scalability of the proposed methods in road sensor networks.

As the promising communication architecture for future networks, the content-centric network (CCN) is still facing many challenges. In CCNs, it is difficult to discover temporary content replicas spreading in routers' caches based on the routing information provided by proactive routing protocols. This paper proposes an advanced perceptive forwarding strategy (APFS), which adaptively perceives closer temporary content replicas to respond the users' requests quickly. A data structure, called a chunk map (CM), is designed and is included into a data packet to indicate the availability of the content replicas in the cache of a closer router. The routers that have received the CM seem to perceive the closer content replicas so that they would intelligently forward the users' requests to the closer routers that cache the requested content replicas. Moreover, a new policy, called early start with punishment, is put forward to guarantee the adaptability of the CM probing and an improved cache replacement policy is employed to extend the validity duration of the CM. The simulation result shows that our APFS scheme has the noticeable performance in terms of the download delay and the average bandwidth.

Energy consumed by network constitutes a significant portion of the total power budget in modern data centers. Thus, it is critical to understand the energy consumption and improve the power efficiency of data center networks (DCNs). In doing so, one straightforward and effective way is to make the size of DCNs elastic along with traffic demands, i.e., turning off unnecessary network components to reduce the energy consumption. Today, software defined networking (SDN), as one of the most promising solutions for data center management, provides a paradigm to elastically control the resources of DCNs. However, to the best of our knowledge, the features of SDN have not been fully leveraged to improve the power saving, especially for large-scale multi-controller DCNs. To address this problem, we propose E3MC, a mechanism to improve DCN's energy efficiency via the elastic multi-controller SDN. In E3MC, the energy optimizations for both forwarding and control plane are considered by utilizing SDN's fine-grained routing and dynamic control mapping. In particular, the flow network theory and the bin-packing heuristic are used to deal with the forwarding plane and control plane, respectively. Our simulation results show that E3MC can achieve more efficient power management, especially in highly structured topologies such as Fat-Tree and BCube, by saving up to 50% of network energy, at an acceptable level of computation cost.

Recent advances in nanotechnology, electronic technology and biology have enabled the development of bio-inspired nanoscale sensors. The cooperation among the bionanosensors in a network is envisioned to perform complex tasks. Clock synchronization is essential to establish diffusion-based distributed cooperation in the bionanosensor networks. This paper proposes a maximum-likelihood estimator of the clock offset for the clock synchronization among molecular bionanosensors. The unique properties of diffusion-based molecular communication are described. Based on the inverse Gaussian distribution of the molecular propagation delay, a two-way message exchange mechanism for clock synchronization is proposed. The maximum-likelihood estimator of the clock offset is derived. The convergence and the bias of the estimator are analyzed. The simulation results show that the proposed estimator is effective for the offset compensation required for clock synchronization. This work paves the way for the cooperation of nanomachines in diffusion-based bionanosensor networks.

With the advances of technologies in Micro-electro-mechanics (MEMS) and wireless communications, it has become feasible to deploy a large-scale wireless sensor network (WSN) with thousands of tiny and inexpensive sensor nodes scattered over a vast field so that information of interest can be obtained, processed, transmitted, and fused automatically via node collaboration and sensing. With the help of cloud computing, Internet of Things (IoTs), device to device (D2D) communications, and big data, cooperative and intelligent sensing will bridge the gap of ubiquitous sensing, intelligent computing, cooperative communication, and mass data management technologies to create novel solutions that improve the urban environment, human life quality, and smart city systems.

With the development of mobile Internet, wireless communication via mobile devices has become a hot research topic, which is typically in the form of Delay Tolerant Networks (DTNs). One critical issue in the development of DTNs is routing. Although there is a lot research work addressing routing issues in DTNs, they cannot produce an advanced solution to the comprehensive challenges since only one or two aspects (nodes’ movements, clustering, centricity and so on) are considered when the routing problem is handled. In view of these defects in the existing works, we propose a novel solution to address the routing issue in social DTNs. By this solution, mobile nodes are divided into different clusters. The scheme, Spray and Wait, is used for the intra-cluster communication while a new forwarding mechanism is designed for the inter-cluster version. In our solution, the characteristics of nodes and the relation between nodes are fully considered. The simulation results show that our proposed scheme can significantly improve the performance of the routing scheme in social DTNs.

Clock synchronization is a very important issue for the applications of wireless sensor networks. The sensors need to keep a strict clock so that users can know exactly what happens in the monitoring area at the same time. This paper proposes a novel internal distributed clock synchronization solution using group neighborhood average. Each sensor node collects the offset and skew rate of the neighbors. Group averaging of offset and skew rate value are calculated instead of conventional point-to-point averaging method. The sensor node then returns compensated value back to the neighbors. The propagation delay is considered and compensated. The analytical analysis of offset and skew compensation is presented. Simulation results validate the effectiveness of the protocol and reveal that the protocol allows sensor networks to quickly establish a consensus clock and maintain a small deviation from the consensus clock.

To achieve effective communication in ad hoc sensor networks, researchers have been working on finding a minimum connected dominating set (MCDS) as a virtual backbone network in practice. Presently, many approximate algorithms have been proposed to construct MCDS, the best among which is adopting the two-stage idea, that is, to construct a maximum independent set (MIS) firstly and then realize the connectivity through the Steiner tree construction algorithm. For the first stage, this paper proposes an improved collaborative coverage algorithm for solving maximum independent set (IC-MIS), which expands the selection of the dominating point from two-hop neighbor to three-hop neighbor. The coverage efficiency has been improved under the condition of complete coverage. For the second stage, this paper respectively proposes an improved Kruskal–Steiner tree construction algorithm (IK–ST) and a maximum leaf nodes Steiner tree construction algorithm (ML-ST), both of which can make the result closer to the optimal solution. Finally, the simulation results show that the algorithm proposed in this paper is a great improvement over the previous algorithm in optimizing the scale of the connected dominating set (CDS).

The control of biofouling on marine vessels is challenging and costly. Early detection before hull performance is significantly affected is desirable, especially if “grooming” is an option. Here, a system is described to detect marine fouling at an early stage of development. In this study, an image of fouling can be transferred wirelessly via a mobile network for analysis. The proposed system utilizes transfer learning and deep convolutional neural network (CNN) to perform image recognition on the fouling image by classifying the detected fouling species and the density of fouling on the surface. Transfer learning using Google’s Inception V3 model with Softmax at last layer was carried out on a fouling database of 10 categories and 1825 images. Experimental results gave acceptable accuracies for fouling detection and recognition.

A delay tolerant network (DTN) presents a new communication model, which uses a store-carry-forward approach to deliver messages due to the nature of the network. With the development of the mobile internet, the research on DTNs becomes a hot topic. One critical issue in the DTNs with social characteristics is that there could exist selfish and malicious nodes which block important data and as a result intentionally disturb the communication. Although there are many research works which address the issue and adopt various trust models to encourage nodes to forward information, most of them have not considered the social characteristics of the mobile nodes and they have not handled the selfish behaviors and attacks in a systematic way. In view of these defects, we propose a novel solution to address the selfish and security issues in social DTNs. Based on nodes’ social characteristics, a dynamic trust model is proposed to prevent bad-mouthing and ballot stuffing attacks and the Shannon entropy function is introduced to avoid blackhole and greyhole attacks. The simulation results show that our proposed scheme can significantly improve the performance of social DTNs. And the simulation is proved to be consistent with the results from theoretical analysis.

Modern vehicles are equipped with a plethora of on-board sensors and large on-board storage, which enables them to gather and store various local-relevant data. However, the wide application of vehicular sensing has its own challenges, among which location-privacy preservation and data query accuracy are two critical problems. In this paper, we propose a novel range query scheme, which helps the data requester to accurately retrieve the sensed data from the distributive on-board storage in vehicular ad hoc networks (VANETs) with location privacy preservation. The proposed scheme exploits structured scalars to denote the locations of data requesters and vehicles, and achieves the privacy-preserving location matching with the homomorphic Paillier cryptosystem technique. Detailed security analysis shows that the proposed range query scheme can successfully preserve the location privacy of the involved data requesters and vehicles, and protect the confidentiality of the sensed data. In addition, performance evaluations are conducted to show the efficiency of the proposed scheme, in terms of computation delay and communication overhead. Specifically, the computation delay and communication overhead are not dependent on the length of the scalar, and they are only proportional to the number of vehicles.