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C-V2X aims at resolving issues pertaining to the traditional usability of V2I and V2V networking. Specifically, C-V2X lowers the number of entities involved in vehicular communications and allows the inclusion of cellular-security solutions to be applied to V2X. For this, the evolvement of LTE-V2X is revolutionary, but it fails to handle the demands of high throughput, ultrahigh reliability, and ultra-low latency alongside its security mechanisms. To counter this, 5G-V2X is considered as an integral solution, which not only resolves the issues related to LTE-V2X but also provides a function-based network setup. Several reports have been given for the security of 5G, but none of them primarily focuses on the security of 5G-V2X and its supportive mobile backhaul. This article provides a detailed overview of 5G-V2X with a security-based comparison to LTE-V2X. A novel Security Reflex Function (SRF)-based architecture is proposed and several research challenges are presented related to the security of 5G-V2X. Furthermore, the article lays out requirements of Ultra-Dense and Ultra-Secure (UD-US) transmissions necessary for 5G-V2X. [ABSTRACT FROM AUTHOR]

Modern fog network architectures, empowered by IoT applications and 5G communications technologies, are characterized by the presence of a huge number of mobile nodes, which undergo frequent handovers, introducing a significant load on the involved network entities. Considering the distributed and flat nature of these architectures, DMM can be the only viable option for efficiently managing handovers in these scenarios. The existing DMM solutions are capable of providing smooth handovers, but lack robustness from the security point of view. Indeed, DMM depends on external mechanisms for handover security and uses a centralized device, which has obvious security and performance implications in flat architectures where hierarchical dependencies can introduce problems. We propose a new DMM schema based on the blockchain, capable of resolving hierarchical security issues without affecting the network layout, and also satisfying fully distributed security requirements with less consumption of energy. [ABSTRACT FROM PUBLISHER]

Urban networks aim at facilitating users for better experience and services through smart platforms such as the Intelligent Transportation System (ITS). ITS focuses on information acquisition, sensing, contrivance control, data processing and forwarding to ground devices via user-specific application-interfaces. The utility of ITS is further improved via the Internet of Things (IoT), which supports "Connectivity to All". One of the key applications of IoT-ITS is urban surveillance. Current surveillance in IoT-ITS is performed via fixed infrastructure-based sensing applications which consume an excessive amount of energy leading to several overheads and failures in the network. Such issues can be overcome by the utilization of on-demand nodes, such as drones, etc. However, drones-assisted surveillance requires efficient communication setup as drones are battery operated and any extemporaneous maneuver during monitoring may result in loss of drone or complete failure of the network. The novelty in terms of network layout can be procured by the utilization of drones with LoRaWAN, which is the protocol designated for Low-PowerWide Area Networks (LPWAN). However, even this architectural novelty alone cannot ascertain the formation of fail-safe, highly resilient, low-overhead, and non-redundant network, which is additionally the problem considered in this paper. To resolve such problem, this paper uses drones as LoRaWAN gateway and proposes a communication strategy based on the area stress, resilient factor, and energy consumption that avail in the efficient localization, improved coverage and energy-efficient surveillance with lower overheads, lower redundancy, and almost zero-isolations. The proposed approach is numerically simulated and the results show that the proposed approach can conserve a maximum of 39.2% and a minimum of 12.6% of the total network energy along with an improvement in the area stress between 89.7% and 53.0% for varying number of drones over a fixed area. [ABSTRACT FROM AUTHOR]

With a more Internet-savvy and sophisticated user base, there are more demands for interactive applications and services. However, it is a challenge for existing radio access networks (e.g. 3G and 4G) to cope with the increasingly demanding requirements such as higher data rates and wider coverage area. One potential solution is the inter-collaborative deployment of multiple radio devices in a 5G setting designed to meet exacting user demands, and facilitate the high data rate requirements in the underlying networks. These heterogeneous 5G networks can readily resolve the data rate and coverage challenges. Networks established using the hybridization of existing networks have diverse military and civilian applications. However, there are inherent limitations in such networks such as irregular breakdown, node failures, and halts during speed transmissions. In recent years, there have been attempts to integrate heterogeneous 5G networks with existing ad hoc networks to provide a robust solution for delay-tolerant transmissions in the form of packet switched networks. However, continuous connectivity is still required in these networks, in order to efficiently regulate the flow to allow the formation of a robust network. Therefore, in this paper, we present a novel network formation consisting of nodes from different network maneuvered by Unmanned Aircraft (UA). The proposed model utilizes the features of a biological aspect of genomes and forms a delay tolerant network with existing network models. This allows us to provide continuous and robust connectivity. We then demonstrate that the proposed network model has an efficient data delivery, lower overheads and lesser delays with high convergence rate in comparison to existing approaches, based on evaluations in both real-time testbed and simulation environment. [ABSTRACT FROM AUTHOR]

M2M (Machine-to-Machine) communication for the Internet of Things (IoTs) system is considered to be one of the major issues in future networks. Considering the characteristics of M2M networks in IoTs systems, traditional security solutions are not able to be applied to E2E (End-to-End) M2M networks because the M2M network itself is vulnerable to various attacks. We consider security aspects for M2M communications and then propose a security gateway application (SGA) including the lightweight symmetric key cryptographic negotiation function, secure E2E M2M key exchange generation function and secure E2E M2M messages delivery function. The proposal of the SGA is newly suggested to improve the gateway application (GA) of the ITU-T M2M service layer in the IoTs reference model. We prove that it could prevent various attacks via the theoretical security analyses. Therefore, it could meet the basic security requirements of the M2M service layer. [ABSTRACT FROM AUTHOR]

In the field of 'U-Healthcare Service', many studies have been actively conducted to develop 'smart device'-based healthcare applications that enable healthcare providers and patients to be better served through interoperations among various kinds of sensors and wireless network interfaces. In particular, contemporary intelligent healthcare services not only recognize users' context information through smart devices, computers, and so forth, but also acquire information from heterogeneous sensors to achieve context-aware inference services. Among such information, the weather information is tightly related to diseases such as asthma and allergies. Therefore, there is a high demand for research to utilize the weather information for healthcare services. In this paper, we propose a context inference-based intelligent healthcare service that exploits both the weather conditions information and the diverse healthcare ontologies available on the Internet. The proposed service aims at modeling a context ontology in users' healthcare service environment and defining the inference rules, thereby accomplishing a satisfactory real-time healthcare service. [ABSTRACT FROM AUTHOR]

The need for continuous monitoring of physiological information of critical organs of the human body, combined with the ever-growing field of electronics and sensor technologies and the vast opportunities brought by 5G connectivity, have made implantable medical devices (IMDs) the most necessitated devices in the health arena. IMDs are very sensitive since they are implanted in the human body, and the patients depend on them for the proper functioning of their vital organs. Simultaneously, they are intrinsically vulnerable to several attacks mainly due to their resource limitations and the wireless channel utilized for data transmission. Hence, failing to secure them would put the patient's life in jeopardy and damage the reputations of the manufacturers. To date, various researchers have proposed different countermeasures to keep the confidentiality, integrity, and availability of IMD systems with privacy and safety specifications. Despite the appreciated efforts made by the research community, there are issues with these proposed solutions. Principally, there are at least three critical problems. (1) Inadequate essential capabilities (such as emergency authentication, key update mechanism, anonymity, and adaptability); (2) heavy computational and communication overheads; and (3) lack of rigorous formal security verification. Motivated by this, we have thoroughly analyzed the current IMD authentication protocols by utilizing two formal approaches: the Burrows–Abadi–Needham logic (BAN logic) and the Automated Validation of Internet Security Protocols and Applications (AVISPA). In addition, we compared these schemes against their security strengths, computational overheads, latency, and other vital features, such as emergency authentications, key update mechanisms, and adaptabilities. [ABSTRACT FROM AUTHOR]

In a typical mobile-sensing scenario, multiple autonomous vehicles cooperatively navigate to maximize the spatial–temporal coverage of the environment. However, as each vehicle can only make decentralized navigation decisions based on limited local observations, it is still a critical challenge to coordinate the vehicles for cooperation in an open, dynamic environment. In this paper, we propose a novel framework that incorporates consensual communication in multi-agent reinforcement learning for cooperative mobile sensing. At each step, the vehicles first learn to communicate with each other, and then, based on the received messages from others, navigate. Through communication, the decentralized vehicles can share information to break through the dilemma of local observation. Moreover, we utilize mutual information as a regularizer to promote consensus among the vehicles. The mutual information can enforce positive correlation between the navigation policy and the communication message, and therefore implicitly coordinate the decentralized policies. The convergence of this regularized algorithm can be proved theoretically under certain mild assumptions. In the experiments, we show that our algorithm is scalable and can converge very fast during training phase. It also outperforms other baselines significantly in the execution phase. The results validate that consensual communication plays very important role in coordinating the behaviors of decentralized vehicles. [ABSTRACT FROM AUTHOR]

Abstract: Transaction processing can guarantee the reliability of business applications. Locking resources is widely used in distributed transaction management (e.g., two phase commit, 2PC) to keep the system consistent. The locking mechanism, however, potentially results in various deadlocks. In service oriented architecture (SOA), the deadlock problem becomes even worse because multiple (sub)transactions try to lock shared resources in the unexpectable way due to the more randomicity of transaction requests, which has not been solved by existing research results. In this paper, we investigate how to prevent local deadlocks, caused by the resource competition among multiple sub-transactions of a global transaction, and global deadlocks from the competition among different global transactions. We propose a replication based approach to avoid the local deadlocks, and a timestamp based approach to significantly mitigate the global deadlocks. A general algorithm is designed for both local and global deadlock prevention. The experimental results demonstrate the effectiveness and efficiency of our deadlock prevention approach. Further, it is also proved that our approach provides higher system performance than traditional resource allocation schemes. [Copyright &y& Elsevier]

Existing binding update (BU) authentication protocols do not consider context information, such as trust, location, and current time, when verifying a mobile node's care-of address (CoA). Instead, the correspondent node executes its own CoA validation in spite of facing a highly trusted situation or simply bypasses the CoA validation, making it difficult to maintain a reasonable trade-off between security and efficiency. This paper applies the context-aware concept to the BU process and proposes a new context-aware ticket-based binding update authentication (caTBUA) protocol. The proposed protocol dynamically performs an appropriate CoA validation based on the context information to achieve a good balance between security and efficiency. Utilizing numerical analysis to compare the performance of the proposed protocol to that of existing authentication protocols in terms of authentication cost and authentication message transmission latency confirmed that the proposed caTBUA protocol yields a better performance than the existing BU authentication protocols. Copyright © 2010 John Wiley & Sons, Ltd. In this paper, we introduce a new context-aware ticket-based binding update authentication (caTBUA) protocol. The caTBUA protocol enables a mobile node and dynamically performs an appropriate CoA validation based on the context information. The presented numerical results corroborate that the proposed caTBUA protocol, which has the ability to select the appropriate authentication method based on the MN's context information, yields a better performance compared with the existing BU authentication protocols while providing a balance between security and efficiency. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including natural and secure computing in modern soft computing application, challenges in monitoring smart cities due to variety of data streams, and embedding intelligent eco-aware systems.

In federated learning (FL), model parameters of deep learning are communicated between clients and the central server. To better train deep learning models, the spectrum resource and transmission security need to be guaranteed. Toward this end, we propose a secrecy transmission protocol based on energy harvesting and jammer selection for FL, in which the secondary transmitters can harvest energy from the primary source. Specifically, a secondary transmitter ST i is first selected, which can offer the best transmission performance for the secondary users to access the primary frequency spectrum. Then, another secondary transmitter ST n , which has the best channel for eavesdropping, is also chosen as a friendly jammer to provide secrecy service. Furthermore, we use outage probability (OP) and intercept probability (IP) as metrics to evaluate performance. Meanwhile, we also derive closed-form expressions of OP and IP of primary users and OP of secondary users for the proposed protocol, respectively. We also conduct a theoretical analysis of the optimal secondary transmission selection (OSTS) protocol. Finally, the performance of the proposed protocol is validated through numerical experiments. The results show that the secrecy performance of the proposed protocol is better than the OSTS and OCJS, respectively. [ABSTRACT FROM AUTHOR]

Smart homes represent one of the principal points in the new ecosystem of the Internet of Things (IoT), both for the centrality of the home in the life of individuals and the significant potential concerning the diffusion of smart objects and innovative services. While IoT-oriented smart homes can revise how inhabitants interact with the domestic environment, each well-defined piece of technology necessitates precise network performance and distinct levels of security based on the sensitivity of the controlled system and the information it handles. This editorial presents a review of the papers accepted in the special issue. The issue has focused at obtaining high-quality papers aimed at solving well-known technical problems and challenges typical of IoT-oriented smart homes. [ABSTRACT FROM AUTHOR]

The seventeenth article, "An Efficient Scheme of Cloud Data Assured Deletion" proposed an efficient scheme of cloud data assured deletion. The twenty-fourth article, "Secure Encrypted Data Deduplication Based on Data Popularity" proposed a secure encrypted data deduplication scheme based on data popularity. The fifteenth article, "A Privacy-preserving Fuzzy Search Scheme Supporting Logic Query over Encrypted Cloud Data" proposed a new secure multi-keyword fuzzy search scheme for encrypted cloud data, their scheme leverages random redundancy method to handle the deterministic of bloom filter to resist SNMF attack. The scheme replaces complicated bilinear pairing with simple scalar multiplication on elliptic curves to realize ciphertext policy attribute-based encryption of cloud data, while solving the security problem of shared data. [Extracted from the article]

Coronavirus disease (COVID-19) has presented extremely harsh requirements on deterministic transmission capability within different communication networks. However, most existing solutions mainly focus on one specific homogeneous scenario, such as pure IP routers or classical medium access control bridges, which leads to many tough challenges in flexibility and scalability. In this article, we propose a framework for heterogeneous deterministic networks (HDNs) to offer low latency and high reliability in tackling epidemics based on smart collaborative theory. Various wired and wireless connecting patterns are supported by utilizing a hybrid mode for COVID-19 relevant operations. Components generated from deterministic networking, time-sensitive networks, and 5G systems have been integrated to achieve corresponding features. The pervasive function translators are available according to virtualization technologies. The protocol-independent and segment-based concepts have been well considered and successfully implemented during our design process. The validations executed on top of the prototype and simulation platform illustrate the advantages of HDN. We aim to enable HDN as an evolutional structure for information exchange in eHealth and other vertical industries. [ABSTRACT FROM AUTHOR]

The advancement of cellular communication technology has profoundly transformed human life. People can now watch high-definition videos anytime, anywhere, and aim for the implementation of advanced autonomous driving capabilities. However, the sustainability of such an environment is threatened by false base stations. False base stations execute attacks in the Radio Access Network (RAN) of cellular systems, adversely affecting the network or its users. To address this challenge, we propose a behavior rule specification-based false base station detection system, SMDFbs. We derive behavior rules from the normal operations of base stations and convert these rules into a state machine. Based on this state machine, we detect network anomalies and mitigate threats. We conducted experiments detecting false base stations in a 5G RAN simulator, comparing our system with seven machine learning-based detection techniques. The experimental results showed that our proposed system achieved a detection accuracy of 98% and demonstrated lower overhead compared to other algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) utilizes algorithms to facilitate intelligent applications across cities in the form of smart-urban projects. As the majority of devices in IoT are battery operated, their applications should be facilitated with a low-power communication setup. Such facility is possible through the Low-Power Wide-Area Network (LPWAN), but at a constrained bit rate. For long-range communication over LPWAN, several approaches and protocols are adopted. One such protocol is the Long-Range Wide Area Network (LoRaWAN), which is a media access layer protocol for long-range communication between the devices and the application servers via LPWAN gateways. However, LoRaWAN comes with fewer security features as a much-secured protocol consumes more battery because of the exorbitant computational overheads. The standard protocol fails to support end-to-end security and perfect forward secrecy while being vulnerable to the replay attack that makes LoRaWAN limited in supporting applications where security (especially end-to-end security) is important. Motivated by this, an enhanced LoRaWAN security protocol is proposed, which not only provides the basic functions of connectivity between the application server and the end device, but additionally averts these listed security issues. The proposed protocol is developed with two options, the Default Option (DO) and the Security-Enhanced Option (SEO). The protocol is validated through Burrows–Abadi–Needham (BAN) logic and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The proposed protocol is also analyzed for overheads through system-based and low-power device-based evaluations. Further, a case study on a smart factory-enabled parking system is considered for its practical application. The results, in terms of network latency with reliability fitting and signaling overheads, show paramount improvements and better performance for the proposed protocol compared with the two handshake options, Pre-Shared Key (PSK) and Elliptic Curve Cryptography (ECC), of Datagram Transport Layer Security (DTLS). [ABSTRACT FROM AUTHOR]

Factory automation is the next industrial revolution. 5G and IIoT are enabling smart factories to seamlessly create a network of wirelessly connected machines and people that can instantaneously collect, analyze, and distribute real-time data. A 5G-enabled communication network for IIOT will boost overall efficiency, launching a new era of market opportunities and economic growth. This article presents the 5G-enabled system architecture and ultra-reliable use cases in smart factories associated with automated warehouses. In particular, for URLLC-based cases, key techniques and their corresponding solutions, including diversity for high reliability, short packets for low latency, and on-the-fly channel estimation and decoding for fast receiver processing, are discussed. Then the channel modeling requirements concerning technologies and systems are also identified in industrial scenarios. Ray tracing channel simulation can meet such requirements well, and based on that, the channel characteristic analysis is presented at 28 and 60 GHz for licensed and unlicensed band frequencies to exploit the available degrees of freedom in the channels. [ABSTRACT FROM AUTHOR]

Highlights • An efficient MAC model is proposed that supports effective timing generation and control over IoD. • Sustainable and secure trajectories are generated by using deep neural networks. • The proposed approach mitigates certain known attacks, such as coagulation attacks on UAVs. • The proposed model dynamically computes the waypoints which eliminate any possibilities of collisions. Abstract Internet of Drones (IoD) involves cooperation and policing amongst Unmanned Aerial Vehicles (UAVs) based on waypoints generated through a series of algorithms. The dynamic reconfigurability of conveyances in IoD demands a secure trajectory and channel accessibility which is procurable through an efficient Medium Access Control (MAC) protocol. The majority of existing works considers the security of UAV-trajectories as a part of cryptographic and channel accessibility only, whereas the proposed approach idealizes link stability as one of the essential components for attaining secure trajectories. Furthermore, this paper proposes a deep neural network-based security framework, which embeds an efficient MAC protocol controlled by Macaulay's duration. The proposed approach fortifies that an extensive control over links provides an adequate duration for enhancing the security aspects of IoD and launching countermeasures against any known cyber attacks. [ABSTRACT FROM AUTHOR]

The Medical Internet-of-Things (MIoT) has developed revolutionary ways of delivering medical care to patients. An example system, showing increasing demand, is the artificial pancreas system that offers convenience and reliable support care to patients with Type 1 Diabetes. Despite the apparent benefits, the system cannot escape potential cyber threats that may worsen a patient's condition. The security risks need immediate attention to ensure the privacy of the patient and preserve safe functionality. Motivated by this, we proposed a security protocol for the APS environment wherein support to essential security requirements is guaranteed, the security context negotiation is resource-friendly, and the protocol is resilient to emergencies. Accordingly, the security requirements and correctness of the design protocol were formally verified using BAN logic and AVISPA, and proved its feasibility through the emulation of APS in a controlled environment using commercial off-the-shelf devices. Moreover, the results of our performance analysis indicate that the proposed protocol is more efficient than the other existing works and standards. [ABSTRACT FROM AUTHOR]

Drone swarming involves multiple unmanned aerial vehicles (UAVs), which are able to manoeuver autonomously, providing a vast range of surveillance applications such as traffic evaluations, driver monitoring, disaster management, temporary network support, and pedestrian tracking. All these applications are an integral part of urban surveillance. In other words, cooperation between multiple drones can facilitate urban surveillance while being able to provide high-quality 3D visualization of the surroundings. This 3D representation is driven by the initiation of accurate and non-overlapping waypoints. Such efficient localization depends on the type of context and its security validation by the receiving entities. For example, a highly burdened context with negligible security is of limited use. This article presents a novel solution that is capable of securing the context information for sharing 3D waypoints between UAVs. The proposed approach achieves optimal localization through hierarchical context- aware aspect-oriented Petri nets while being powered by a novel drone context-exchange protocol for security validations. [ABSTRACT FROM AUTHOR]

Compared to traditional networking, SDN has better controllability and visibility for network components, which enable better management by using the common controller. In this article, the standard architecture of SDN is enhanced to utilize UAVs as on-demand forwarding switches. The proposed approach can achieve efficient management and fast handovers by decreasing the handover latency, E2E delay, and signaling overheads. The illustrated scenarios will help in understanding the impact of existing handover approaches in the next generation wireless networks, especially the upcoming 5G, which includes small cells, UAVs, UEs, and so on. The simulation study shows that scenarios with both UAVs and small cells perform better than scenarios with only small cells. The results in this article show that the proposed SDNbased handover scenarios perform better than the existing 4G-LTE handover for UAVs. [ABSTRACT FROM AUTHOR]

5G-assisted device-to-device (D2D) communication plays an instrumental role in minimizing latency, maximizing resource utilization, improving speed, and boosting system capacity. However, the technology confronts several challenges to realize its enormous potential fully. Security and privacy concerns are at the top of the list that can jeopardize the regular operation of D2D communication by executing various assaults such as free-riding and impersonation. Although several researchers suggested different solutions to these concerns, most are too heavy for resource-constrained devices or are vulnerable to security risks. Consequently, we proposed a lightweight and provably secure D2D communication protocol comprising initialization, device discovery, and link setup phases. The protocol is light in terms of computational overhead and communication latency while verifiably secure through formal security analysis. The protocol relies on a new network function, called D2D Security Management Function (DSMF), located near the devices to facilitate secure communication and improve performance. Moreover, we used deep learning-based UE trust score forecasting to better handle and prioritize communication requests when the network is overloaded. The comparative analysis against state-of-the-art security schemes concerning computational and communication overheads shows that our protocol is a superior alternative for resource-constrained IoT devices wishing to perform D2D communication in a 5G network. [ABSTRACT FROM AUTHOR]

The development of new technologies such as the Internet of Things and cloud computing tests the transmission capabilities of communication networks. With the widespread application of multiple wireless access technologies, it has become popular for modern communication devices to be equipped with multiple network access interfaces. The increasing of various network attacks significantly reduces the robustness of multipath TCP (MPTCP) transport systems. To address this problem, this paper proposes a network traffic anomaly detection model based on MPTCP networks, called MPTCP-EMD. The model combines multi-scale detection and digital signal processing theory to implement anomaly detection based on the self-similarity of MPTCP network traffic. It uses the empirical modal decomposition (EMD) method to decompose MPTCP traffic data and reconstruct the valid signal by removing high-frequency noise and residual trend term. Using the idea of sliding windows, the model then compares the changes in the Hurst exponent of the MPTCP network under different attack conditions to determine whether anomalies have occurred. The simulation results show that the EMD method can be used for anomaly detection of MPTCP network traffic. The Hurst exponent of the attacked MPTCP network significantly exceeds the range of the unattacked network, and exhibits significant jitter. [ABSTRACT FROM AUTHOR]

The popularity of mobile devices, especially intelligent mobile phones, significantly prompt various location-based services (LBSs) in cloud systems. These services not only greatly facilitate people's daily lives, but also cause serious threats that users' location information may be misused or leaked by service providers. The dummy-based privacy protection techniques have significant advantages over others because they neither rely on trusted servers nor need adequate number of trustworthy peers. Existing dummy-based location privacy protection schemes, however, cannot yet provide long-term privacy protection. In this paper, we propose four principles for the dummy-based long-term location privacy protection (LT-LPP). Based on the principles, we propose a set of long-term consistent dummy generation algorithms for the LT-LPP. Our approach is built on soft computing techniques and can balance the preferred privacy protection and computing cost. Comprehensive experimental results demonstrate that our approach is effective to both long-term privacy protection and fake path generation for LBSs in mobile clouds. [ABSTRACT FROM AUTHOR]

The article discusses papers published within the issue, including "A Soft Computing Based Location-Aware Access Control for Smart Buildings," by J. L. Hernandez et al. and "Towards Cognitive Economy," by Lidia Ogiela.

5G-assisted device-to-device (D2D) communication plays an instrumental role in minimizing latency, maximizing resource utilization, improving speed, and boosting system capacity. However, the technology confronts several challenges to realize its enormous potential fully. Security and privacy concerns are at the top of the list that can jeopardize the regular operation of D2D communication by executing various assaults such as free-riding and impersonation. Although several researchers suggested different solutions to these concerns, most are too heavy for resource-constrained devices or are vulnerable to security risks. Consequently, we proposed a lightweight and provably secure D2D communication protocol comprising initialization, device discovery, and link setup phases. The protocol is light in terms of computational overhead and communication latency while verifiably secure through formal security analysis. The protocol relies on a new network function, called D2D Security Management Function (DSMF), located near the devices to facilitate secure communication and improve performance. Moreover, we used deep learning-based UE trust score forecasting to better handle and prioritize communication requests when the network is overloaded. The comparative analysis against state-of-the-art security schemes concerning computational and communication overheads shows that our protocol is a superior alternative for resource-constrained IoT devices wishing to perform D2D communication in a 5G network. [ABSTRACT FROM AUTHOR]

The development of Information and Communication Technologies (ICT) has now reached some entirely unexpected domains. Many applications in modern cities lead to novelties, resulting in new habits of the citizens of tomorrow's smart cities. These innovations include Intelligent Transportation Systems (ITS). An important application domain of ITS is undoubtedly represented by the dynamic and optimized management of traffic-lighted road intersections. Although several works have already been presented in the literature over the years, many have not considered the new perspectives regarding the source of data to manage traffic lights, i.e., not only Wireless Sensor Networks (WSNs), but, mainly, the vehicular communications. This paper introduces an innovative approach to dynamically regulating traffic light cycles and phases in an isolated intersection. The suggested method can fit data management from WSNs and vehicular communications through IEEE 802.11p and LTE-V2V, employing various Fuzzy Logic Controllers (FLCs) that manage vehicles turning movements for dynamic controls of both the phase and the green time of traffic lights. The results obtained will allow us to observe that the proposed application is better than the others. Each of the implemented configurations will bring advantages and disadvantages that allow choosing one configuration or another based on specific project requirements. [ABSTRACT FROM AUTHOR]

In broadband wireless technology, due to having many salient advantages, such as high data rates, quality of service, scalability, security, mobility, etc., LTE-A currently has been one of the trends of wireless system development. This system provides several sophisticated authentication and encryption techniques to enhance its system security. However, LTE-A still suffers from various attacks, like eavesdropping and replay attacks. Therefore, in this paper, we propose a novel security scheme, called the security system for a 4G environment (Se4GE for short), which as an LTE-A-based system integrates the RSA and Diffie-Hellman algorithms to solve some of LTE-A's security drawbacks where LTE-A stands for LTE-Advance which is a 4G system. The Se4GE is an end-to-end ciphertext transfer mechanism which dynamically changes encryption keys to enforce the security of data transmission in an LTE-A system. The Se4GE also produces several logically connected random keys, called the intelligent protection-key chain, which invokes two encryption/decryption techniques to provide users with broader demands for security services. The analytical results show that the Se4GE has higher security level than that of an LTE-A system. [ABSTRACT FROM AUTHOR]

IPv6 mobility management is one of the most challenging research topics for enabling mobility service in the forthcoming mobile wireless ecosystems. The Internet Engineering Task Force has been working for developing efficient IPv6 mobility management protocols. As a result, Mobile IPv6 and its extensions such as Fast Mobile IPv6 and Hierarchical Mobile IPv6 have been developed as host-based mobility management protocols. While the host-based mobility management protocols were being enhanced, the network-based mobility management protocols such as Proxy Mobile IPv6 (PMIPv6) and Fast Proxy Mobile IPv6 (FPMIPv6) have been standardized. In this paper, we analyze and compare existing IPv6 mobility management protocols including the recently standardized PMIPv6 and FPMIPv6. We identify each IPv6 mobility management protocol's characteristics and performance indicators by examining handover operations. Then, we analyze the performance of the IPv6 mobility management protocols in terms of handover latency, handover blocking probability, and packet loss. Through the conducted numerical results, we summarize considerations for handover performance. [ABSTRACT FROM PUBLISHER]

Abstract: Current research on UHF RFID system security mainly focus on protecting communication safety and information privacy between a pair of specific tags and its corresponding interrogation reader. However, instead of stealing detailed private information of tags, adversaries may just want to estimate the cardinality of tags, which is named counting attack. Unfortunately, most existing protocols are vulnerable to counting attack. To defend against this attack, in this paper we propose ACSP, a novel Anti-Counting Security Protocol. ACSP employs session identifier and provides a corresponding authentication metric to verify the commands sent by the reader. To handle counting attack, ACSP periodically updates the session identifier, and securely identifies tags with encryption. We evaluate the performance of ACSP through theoretical analysis and qualitative comparison. Results show that ACSP can efficiently withstand counting attack as well as defending against regular security threats as existing protocols. [Copyright &y& Elsevier]

Abstract: In this paper a new energy-aware weighted dynamic topology control (WDTC) algorithm is proposed to extend the lifetime of wireless network and balance the nodes’ energy consumption. The idea is that each node builds its local minimum spanning tree (MST) based on the energy-aware weighted graph and the network topology is adjusted accordingly. It was proved theoretically that the topology under WDTC algorithm could preserve the network connectivity and a sufficient condition for the degree of no more than 6 was also given. Simulation shows that WDTC algorithm can effectively prolong the network lifetime and has good topological features. [Copyright &y& Elsevier]

Abstract: Cognitive radio technique is the next step toward efficient wireless bandwidth utilization. While some of the spectrum bands (unlicensed band) have been increasingly used, most of the other spectrum resources (licensed band) are underutilized. This drives the challenges of open spectrum and dynamic spectrum access concepts, which allows unlicensed users (or called secondary users, SUs) equipped with cognitive radios to opportunistically access the spectrum not used by licensed users (or called primary users, PUs). Most existing results mainly focus on designing the lower-layer cognitive radio problems. In the literature, this is the first result to investigate the higher-layer solution for cognitive radio networks. In this paper, we present a cross-layer protocol of spectrum mobility (layer-2) and handover (layer-3) in cognitive LTE networks. With the consideration of the Poisson distribution model of spectrum resources, a cross-layer handoff protocol with the minimum expected transmission time is developed in cognitive LTE networks. Performance analysis of the proposed handoff protocol is investigated. Finally, simulation results illustrates the proposed handoff protocol significantly reduces the expected transmission time and the spectrum mobility ratio. [Copyright &y& Elsevier]

Generally, the 5th Generation (5G) network will be soon available in the near future. It will be one with the feature that some of its network functions are handled by Virtual Machines (VMs), rather than by a dedicated one (like that in the 4th generation (4G) networks). Basically, VMs of a specific network function are managed by their own Software Defined Networking (SDN) Controller, which makes the management and operation of these VMs and OpenvSwitches become easier. On the other hand, the connection between Internet of Things (IoT) devices and a network is often intermittent. Consequently, Authentication Server Function (AUSF) has to frequently authenticate these devices. This is also one of the reasons why a 5G network will prepare an AUSF Pool to distribute its authentication burden, particularly when a huge number of user equipment (UEs) and IoT devices are connected to it. Next, when a VM fails, how do other AUSFs take over for the failed to continue the authentication tasks? This is not completely defined in 5G specifications. In this study, we propose a fault tolerant mechanism for 5G end-device authentication, named Fault Tolerant 5G Authentication Scheme (FT5AS), in which a machine, named Mediator, is added to manage and keep track of authentication steps for end devices. The purpose is that when a VM fails, other AUSFs can successfully take over its authentication tasks on UEs. Also, the FT5AS can detect this failure immediately and react properly, aiming to increase the Quality of Service (QoS) that an UE can receive from 5G networks. Our simulation results indicate that the FT5AS is feasible and its performance is not lower than that of tested schemes. [ABSTRACT FROM AUTHOR]

With the recent advances in mobile technologies, biometric verification is being adopted in many smart devices as a means for authenticating their owners. As biometric data leakage may cause stringent privacy issues, many proposals have been offered to guarantee the security of stored biometric data, i.e., biometric template. One of the most promising solutions is the use of a remote server that stores the template in an encrypted form and performs a biometric comparison on the ciphertext domain, using recently proposed functional encryption (FE) techniques. However, the drawback of this approach is that considerable computation is required for the inner-pairing product operation used for the decryption procedure of the underlying FE, which is performed in the authentication phase. In this paper, we propose an enhanced method to accelerate the inner-pairing product computation and apply it to expedite the decryption operation of FE and for faster remote biometric verification. The following two important observations are the basis for our improvement—one of the two arguments for the decryption operation does not frequently change over authentication sessions, and we only need to evaluate the product of multiple pairings, rather than individual pairings. From the results of our experiments, the proposed method reduces the time required to compute an inner-pairing product by 30.7%, compared to the previous best method. With this improvement, the time required for biometric verification is expected to decrease by up to 10.0%, compared to a naive method. [ABSTRACT FROM AUTHOR]

Cybersecurity in Industrial Internet of Things (IIoT) has become critical as smart cities are becoming increasingly linked to industrial control systems (ICSs) used in critical infrastructure. Consequently, data-driven security systems for analyzing massive amounts of data generated by smart cities have become essential. A representative method for analyzing large-scale data is the game bot detection approach used in massively multiplayer online role-playing games. We reviewed the literature on bot detection methods to extend the anomaly detection approaches used in bot detection schemes to IIoT fields. Finally, we proposed a process wherein the data envelopment analysis (DEA) model was applied to identify features for efficiently detecting anomalous behavior in smart cities. Experimental results using random forest show that our extracted features based on a game bot can achieve an average F1-score of 0.99903 using 10-fold validation. We confirmed the applicability of the analyzed game-industry methodology to other fields and trained a random forest on the high-efficiency features identified by applying a DEA, obtaining an F1-score of 0.997 using the validation set approach. In this study, an anomaly detection method for analyzing massive smart city data based on a game industry methodology was presented and applied to the ICS dataset. [ABSTRACT FROM AUTHOR]

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) allow better coverage, enhanced connectivity, and elongated lifetime when used in telecommunications. However, these features are predominately affected by the policies used for sharing resources amongst the involved nodes. Moreover, the architecture and deployment strategies also have a considerable impact on their functionality. Recently, many researchers have suggested using layer-based UAV deployment, which allows better communications between the entities. Regardless of these solutions, there are a limited number of studies which focus on connecting layered-UAVs to everything (U2X). In particular, none of them have actually addressed the aspect of resource allocation. This paper considers the issue of resource allocation and helps decide the optimal number of transfers amongst the UAVs, which can conserve the maximum amount of energy while increasing the overall probability of resource allocation. The proposed approach relies on mutual-agreement based reward theory, which considers Minkowski distance as a decisive metric and helps attain efficient resource allocation for backhaul-aware U2X. The effectiveness of the proposed solution is demonstrated using Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

This paper presents a novel dissimilarity metric based on local neighboring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios. [ABSTRACT FROM AUTHOR]

The rapid growth in the transportation sector has led to the emergence of smart vehicles that are equipped with ICT. These modern smart vehicles are connected to the Internet to access various services such as road condition information, infotainment, and energy management. This kind of scenario can be viewed as a vehicular cyber-physical system (VCPS) where the vehicles are at the physical layer and services are at the cyber layer. However, network traffic management is the biggest issue in the modern VCPS scenario as the mismanagement of network resources can degrade the quality of service (QoS) for end users. To deal with this issue, we propose a software defined networking (SDN)-enabled approach, named SeDaTiVe, which uses deep learning architecture to control the incoming traffic in the network in the VCPS environment. The advantage of using deep learning in network traffic control is that it learns the hidden patterns in data packets and creates an optimal route based on the learned features. Moreover, a virtual-controller-based scheme for flow management using SDN in VCPS is designed for effective resource utilization. The simulation scenario comprising 1000 vehicles seeking various services in the network is considered to generate the dataset using SUMO. The data obtained from the simulation study is evaluated using NS-2, and proves that the proposed scheme effectively handles real-time incoming requests in VCPS. The results also depict the improvement in performance on various evaluation metrics like delay, throughput, packet delivery ratio, and network load by using the proposed scheme over the traditional SDN and TCP/IP protocol suite. [ABSTRACT FROM AUTHOR]

Software filtering systems can be employed to detect and filter out pirated or counterfeit software on the Web sites and peer-to-peer networks. They determine whether a suspicious program is legal or not by comparing it with original programs in a database or in the market. To identify pirated or counterfeit software, software filtering systems need to measure software similarity when comparing a suspicious program with original ones. In this case, the comparison overhead might be very high because the suspicious program is compared with all programs in the database or market in the worst case. This paper proposes a software classification scheme for efficient software filtering systems. The scheme focuses specifically on the Windows portable executable files which have been prime targets for software pirates. The scheme extracts software characteristics from a suspicious program and classifies it into one of pre-defined categories quickly based on the characteristics. The suspicious program is compared only with the programs in the one of pre-defined categories in most cases; thus, the comparison overhead is reduced. We propose two classification methods. The first one extracts strings from GUI-related resources of a program and computes the relevance of the program to each category based on the pre-computed score of the strings. The second one extracts API call frequency from a program's execution codes and uses Random Forest technique to classify the program. Experimental results show that the proposed scheme can classify programs effectively and can reduce the comparison overhead significantly. [ABSTRACT FROM AUTHOR]