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The rapid evolution of mobile and optical communication technologies is driving the transition from 5G to 6G networks. This transition inevitably brings about changes in authentication scenarios, as new security demands emerge that go beyond the capabilities of existing frameworks. Therefore, it is necessary to address these evolving requirements and the associated key challenges: ensuring Perfect Forward Secrecy (PFS) to protect communications even if long-term keys are compromised and integrating Post-Quantum Cryptography (PQC) techniques to defend against the threats posed by quantum computing. These are essential for both radio and optical communications, which are foundational elements of future 6G infrastructures. The DMRN Protocol, introduced in 2022, represents a major advancement by offering both PFS and PQC while maintaining compatibility with existing 3rd Generation Partnership Project (3GPP) standards. Given the looming quantum-era challenges, it is imperative to analyze the protocol’s security architecture through formal verification. Accordingly, we formally analyze the DMRN Protocol using SVO logic and ProVerif to assess its effectiveness in mitigating attack vectors, such as malicious or compromised serving networks (SNs) and home network (HN) masquerading. Our research found that the DMRN Protocol has vulnerabilities in key areas such as mutual authentication and key exchange. In light of these findings, our study provides critical insights into the design of secure and quantum-safe authentication protocols for the transition to 6G networks. Furthermore, by identifying the vulnerabilities in and discussing countermeasures to address the DMRN Protocol, this study lays the groundwork for the future standardization of secure 6G Authentication and Key Agreement protocols.

5G acts as a highway enabling innovative digital transformation and the Fourth Industrial Revolution in our lives. It is undeniable that the success of such a paradigm shift hinges on robust security measures. Foremost among these is primary authentication, the initial step in securing access to 5G network environments. For the 5G primary authentication, two protocols, namely 5G Authentication and Key Agreement (5G-AKA) and Improved Extensible Authentication Protocol Method for 3rd Generation Authentication and Key Agreement (EAP-AKA′), were proposed and standardized, where the former is for 3GPP devices, and the latter is for non-3GPP devices. Recent scrutiny has unveiled vulnerabilities in the 5G-AKA protocol, exposing it to security breaches, including linkability attacks. Moreover, mobile communication technologies are dramatically evolving while 3GPP has standardized Authentication and Key Management for Applications (AKMA) to reuse the credentials, generated during primary authentication, for 5G network applications. That makes it so significant for 5G-AKA to be improved to support forward secrecy as well as address security attacks. In response, several protocols have been proposed to mitigate these security challenges. In particular, they tried to strengthen security by reusing secret keys negotiated through the Elliptic Curve Integrated Encryption Scheme (ECIES) and countering linkability attacks. However, they still have encountered limitations in completing forward secrecy. Motivated by this, we propose an augmentation to 5G-AKA to achieve forward security and thwart linkability attacks (called 5G-AKA-FS). In 5G-AKA-FS, the home network (HN), instead of using its static ECIES key pair, generates a new ephemeral key pair to facilitate robust session key negotiation, truly realizing forward security. In order to thoroughly and precisely prove that 5G-AKA-FS is secure, formal security verification is performed by applying both BAN Logic and ProVerif. As a result, it is demonstrated that 5G-AKA-FS is valid. Besides, our performance comparison highlights that the communication and computation overheads are intrinsic to 5G-AKA-FS. This comprehensive analysis showcases how the protocol effectively balances between security and efficiency.

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.

As 5G telecom services evolve rapidly across a broad technological environment, network security in 5G landscape emerges as a critically challenging issue. One of typical network security tools is an intrusion prevention system (IPS) that monitors a network for malicious activity across the cyber-attack chain and takes action to prevent it. Vulnerabilities in 5G core networks become more varied and protocols become increasingly complex, whereby conventional Next Generation Firewall (NGFW) is not enough anymore to respond to cyber attacks. As a typical 5G vulnerability attack, PFCP-in-GTP and IPSec disable attack are highly complex to detect and cannot identify attackers without integrated session management. However, the 5G core network uses various protocols such as Non-Access Stratum (NAS), Hyper Text Transfer Protocol (HTTP), Packet Forwarding Control Protocol (PFCP), and GPRS Tunnelling Protocol (GTP), and packets of the interface used by each protocol are managed as identities that are difficult to identify. Analyzing the relationship of these interfaces in real time is an important key to integrated session management. In addition, unlike existing 4G, as 3rd Generation Partnership Project (3GPP) specs mandate encrypting 5G Standalone (SA) user IDs, it is much more difficult to identify from which user traffic has occurred in IPSs exclusive for cellular network. With regard to the above subject, this paper introduces an efficient session management scheme for users not affordable in conventional NFGW but necessarily useful for security systems in 5G SA. Furthermore, this study compared performances between conventional NGFWs and a 5G IPS system with the scheme employed, to ascertain that the scheme is feasibly implementable in 5G SA network. The actual test results show a detection rate of 99.7% and reasonable resource overhead (Memory usage 37.8%, CPU usage 42–44%).

After decades of rapid development, the scale and complexity of modern networks have far exceed our expectations. In many conditions, traditional traffic identification methods cannot meet the demand of modern networks. Recently, fine-grained network traffic identification has been proved to be an effective solution for managing network resources. There is a massive increase in the use of fine-grained network traffic identification in the communications industry. In this article, we propose a comprehensive overview of fine-grained network traffic identification. Then, we conduct a detailed literature review on fine-grained network traffic identification from three perspectives: wired network, mobile network, and malware traffic identification. Finally, we also draw the conclusion on the challenges of fine-grained network traffic identification and future research prospects.

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.

Mobile edge computing which provides computing capabilities at the edge of the radio access network can help smart home reduce response time. However, the limited computing capacity of edge servers is the bottlenecks for the development of edge computing. We integrate cloud computing and edge computing in the Internet of Things to expand the capabilities. Nevertheless, the cost of leasing computing resources has been seldom considered. In this paper, we study the joint transmission power and resource allocation to minimize the users’ overhead which is measured by the sum of energy consumption and cost leasing servers. We formulate the problem as a Mixed Integer Linear Programming which is NP-hard and present the Branch-and-Bound to solve it. Due to high complexity, a learning method is proposed to accelerate the algorithm. The branching policy can be learned to reduce the time-cost of the Branch-and-Bound algorithm. Simulation results show our approach can improve the Branch-and-Bound efficiency and performs closely to the traditional branching scheme.

Cellular technology has evolved over the decades for mobile network operators to accommodate the ever-growing demands of services for connecting Vehicle-to-Everything (V2X). The 5G infrastructure facilitates V2X communications, where a small-cell base station operating at ultra-high radio frequency with limited coverage becomes pervasive. These small-cell base stations in 5G-V2X must be strategically deployed near the consumers to realize several use cases. More recently, the architectural split solutions in Next Generation Radio Access Network (NG-RAN) are introduced, in which the gNB is divided into the distributed unit (gNB-DU) and control unit (gNB-CU). This functional split intends to improve scalability, performance, and network orchestration optimization. In this case, frequent user equipment (UE) handover between gNB-DUs is inevitable. However, the current 5G standard did not consider securing the path between these two entities. Hence, the NG-RAN could likely experience various security threats if the current handover procedure standard is employed without changes. Consequently, this paper introduces potential threats like resource depletion at NG-RAN caused by the useless execution of resource-demanding procedures to complete the transfer of attachment of UE to target gNB-DU. Another is UE being denied from accessing services caused by unsuccessful uplink and downlink synchronization during random access procedure execution, requiring establishing security and mutual authentication between the entities. Motivated by this, we proposed a security protocol composed of two phases, namely initial and handover. While the former phase assists in mutual authentication and key agreement between UE and serving gNB-DU, the latter secures UE’s mobility in inter-gNB-DU handover. This protocol aims to preserve the existing quality of service and support essential security requirements, including confidentiality, integrity, mutual authentication, secure key exchange, and perfect forward secrecy. The security requirements are formally verified using BAN logic and Scyther, and the proposed protocol demonstrated lower handover latency than EAP-AKA’, AKA, EAP-TLS, and EAP-IKEv2.

In the industrial Internet of things (IIoT), there is always a strong demand for real-time information transfer. Especially when deploying wireless/wired hybrid networks in smart factories, the requirement for low delay interaction is more prominent. Although tree routing protocols have been successfully executed in simple networks, more challenges in transmission speed can be observed in the manufacturing broadband communication system. Motivated by the progresses in deep learning, a smart collaborative routing protocol with low delay and high reliability is proposed to accommodate mixed link scenarios. First, we establish a one-hop delay model to investigate the potential affects of Media Access Control (MAC) layer parameters, which supports the subsequent design. Second, forwarding, maintenance, and efficiency strategies are created to construct the basic functionalities for our routing protocol. Relevant procedures and key approaches are highlighted as well. Third, two sub-protocols are generated and the corresponding implementation steps are described. The experimental results demonstrate that the end-to-end delay can be effectively cut down through comprehensive improvements. Even more sensor nodes and larger network scale are involved, our proposed protocol can still illustrate the advantages comparing with existing solutions within IIoT.

Figuring the network's hidden abnormal behavior can reduce network vulnerability. This paper presents a detailed architecture in which the collected log data of the network can be processed and analyzed. We process and integrate on-campus network information from every router and store the integrated NetFlow log data. Ceph is used as an open-source distributed storage platform that offers high efficiency, high reliability, scalability, and preliminary preprocessing of raw data with Python, removing redundant areas and unification. In the subanalysis, we discover the anomaly event and absolute flow by three times of standard deviation rule. Keras has been used to classify in-time data collected via a cyber-attack and to construct an automatic identifier template through the Recurring Neural Network (RNN) test. The identification accuracy of the optimization model is around 98% in attack detection. Finally, in the MySQL server, the results of the real-time evaluation can be obtained, and the results of the assessment can be displayed via ECharts.

In future engineered systems for medical applications, a tight real-time integration between physical and computational processes will be required. That integration is achieved using feedback control loops which need high quality input data streams. However, hardware platforms can barely provide such high-quality data sequences (especially if mobile nodes are considered), and mechanisms to improve and polish physical and biological signals are then necessary. This paper proposes a predictor-corrector algorithm to improve the quality and precision of data (biological) signals in Internet of Medical Things deployments, especially if composed of mobile nodes. The proposed algorithm employs an Artificial Intelligence approach and statistical learning techniques to predict future data samples and correct errors in received information. Employed mathematical models follow a prediction-correction scheme and are based on complex functions, Laurent series and the idea of complex envelope. Simulation techniques are used to evaluate the performance of the proposed solution, showing that it improves the precision of traditional linear interpolation techniques up to 85%, and cubic splines up to 20%. Processing delay during operation is, for the referred precision, around 200ms.

With the development of various deep learning algorithms, the importance and potential of AI + medical treatment are increasingly prominent. Electrocardiogram (ECG) as a common auxiliary diagnostic index of heart diseases, has been widely applied in the pre-screening and physical examination of heart diseases due to its low price and non-invasive characteristics. Currently, the multi-lead ECG equipments have been used in the clinic, and some of them have the automatic analysis and diagnosis functions. However, the automatic analysis is not accurate enough for the discrimination of abnormal events of ECG, which needs to be further checked by doctors. We therefore develop a deep-learning-based approach for multi-label classification of ECG named Multi-ECGNet, which can effectively identify patients with multiple heart diseases at the same time. The experimental results show that the performance of our methods can get a high score of 0.863 (micro-F1-score) in classifying 55 kinds of arrythmias, which is beyond the level of ordinary human experts.

In the 5G era, network mobility management is recognized as a very important factor for user service availability. Especially, due to fast speed and shrinking cell coverage, frequent handover is expected than before. Hence, efficient handover procedure is essential to guarantee seamless service to users. Distributed IP Mobility Management (DMM), a major mobility management solution, is a flat architecture that achieves efficiency and fault tolerance by excluding a centralized anchor and minimizing the distance between a mobile device and its serving network. However, DMM, which has no dominant security scheme specified to itself, is excessively dependent on the security of Layer 2 and is vulnerable to various threats. Especially, the existing security schemes are still venerable to redirection attacks launched by malicious Mobile Access Gateways (MAGs) or Control Mobility Database (CMD). Motivated by this, we proposed a DMM-based handover security protocol that can support privacy and defend against redirection attacks in addition to providing essential security properties such as confidentiality, integrity, mutual authentication, and key exchange. The proposed protocol was formally verified to be correct through AVISPA and BAN logic. Moreover, the comparison analysis showed that the proposed protocol is better than the previous studies and standards.

Recently, Satellite Internet of Things (SIoT), a space network that consists of numerous Low Earth Orbit (LEO) satellites, is regarded as a promising technique since it is the only solution to provide 100% global coverage for the whole earth, without any additional terrestrial infrastructure supports. However, compared with Geostationary Earth Orbit (GEO) satellites, the LEO satellites always move very fast to cover an area within only 5-12 minutes per pass, bringing high dynamics to the network access. Furthermore, to reduce the cost, the power and spectrum channel resources of each LEO satellite are very limited, i.e., less than 10% of GEO. Therefore, to take fully advantage of the limited resource, it is very challenging to have an efficient resource allocation scheme for SIoT. Current resource allocation schemes for satellites are mostly designed for GEO, and these schemes do not consider many LEO specific concerns, including the constrained energy, the mobility characteristic, the dynamics of connections and transmissions etc. Towards this end, we proposed DeepCA, a novel reinforcement learning based approach for energy-efficient channel allocation in SIoT. In DeepCA, we firstly introduce a new sliding block scheme to facilitate the modeling of dynamic feature of the LEO satellite, and formulate the dynamic channel allocation problem in SIoT as a Markov decision process (MDP). We then propose a deep reinforcement learning algorithm for optimal channel allocation. To accelerate the learning process of DeepCA, we utilize the image form to represent the requests of users to reduce the input size, and carefully divide an action into multiple mini-actions to reduce the size of the action set. Extensive simulations show that our proposed DeepCA approach can save at least 67.86% energy consumption compared with traditional algorithms.

Driven by the ubiquitous smart devices, wireless traffic data has increased significantly and brings a great burden on backhaul links. Edge caching and device-to-device are proved not only can overcome the above challenges, but also to reduce the energy consumption of devices. Meanwhile, the development of hardware makes it possible for terminal devices to cache popular content (referred to as “helpers”). In this paper, a device-to-device assisted edge caching network via helpers is designed to provide low-latency content access and alleviate the traffic load. Then, we formulate the problem of maximization of the successful delivery rate under the constraints of the Signal-to-Interference-plus-Noise Ratio and caching capacity as an integer programming and pose a novel efficient and effective scheme. Based on the algorithm, we first transform the problem into a tractable form. Then this paper presents a hybrid of heuristic approach and iterative selection method to limit the search scope. The simulations demonstrate the method can significantly reduce the system computation time. Moreover, the effects of different scenarios and parameter settings about the successful delivery rate of the Device-to-Device assisted edge caching networks are analyzed.

With an enormous range of applications, the Internet of Things (IoT) has magnetized industries and academicians from everywhere. IoT facilitates operations through ubiquitous connectivity by providing Internet access to all the devices with computing capabilities. With the evolution of wireless infrastructure, the focus from simple IoT has been shifted to smart, connected and mobile IoT (M-IoT) devices and platforms, which can enable low-complexity, low-cost and efficient computing through sensors, machines, and even crowdsourcing. All these devices can be grouped under a common term of M-IoT. Even though the positive impact on applications has been tremendous, security, privacy and trust are still the major concerns for such networks and insufficient enforcement of these requirements introduces non-negligible threats to M-IoT devices and platforms. Thus, it is important to understand the range of solutions which are available for providing a secure, privacy-compliant, and trustworthy mechanism for M-IoT. There is no direct survey available, which focuses on security, privacy, trust, secure protocols, physical layer security and handover protections in M-IoT. This paper covers such requisites and presents comparisons of state-the-art solutions for IoT which are applicable to security, privacy, and trust in smart and connected M-IoT networks. Apart from these, various challenges, applications, advantages, technologies, standards, open issues, and roadmap for security, privacy and trust are also discussed in this paper.

Promoted by the advancements in the various wireless access technologies, modern mobile devices equipped with multiple network interfaces are rapidly becoming the norm, and this provides a driving force for the large-scale deployment of the Multipath Transmission Control Protocol (MPTCP) in the current and future Internet. However, the simultaneous use of multiple network paths for concurrent multipath data transmission can make MPTCP have a larger attack surface than the traditional single-path transport protocols, and this may be likely to pose a risk of MPTCP being much more susceptible to cyber attacks. In this paper, we present a measurement method to investigate the vulnerability and robustness of MPTCP under cyber attacks with incomplete network information, by considering the fact that most cyber attacks normally lack of real-time information with respect to various MPTCP attributes. We mathematically characterize cyber attacks with incomplete network information from the viewpoints of both the cyber attacker and the MPTCP communication system, and then we introduce a mixed attack strategy, by jointly considering the features of both the random attacks and the selective attacks, to evaluate the robustness of MPTCP.

Green communications, focusing on energy efficiency, is a hot topic in both academic and industry communities since they can significantly improve sustainability concerning power resources and environmental conditions. Later, with much focus on this area, several green communication methods started enrolling into a more thoughtful phase, with compelling applications in several networks. For instance, a simple and effective green communication solution is to arrange a sleep mode device incorporated by several MAC protocols with broad wireless network applications. However, it is required to examine the trade-off between green communications’ energy efficiency and network requirements. In addition, it is crucial to appraise the performance concerning the energy consumption, the throughput, and the response time, regarding green communications under different wireless network conditions (e.g., different traffic and different channels).

Smart farming is rapidly revolutionizing the agricultural sector where embedded Internet of Things (IoT) devices are integrated into the field to maintain or improve the quality of products as well as increase food production. Despite the tremendous benefits, various cybersecurity threats of IoT can also be inherited by the sector. In this paper, we propose a lightweight specification-based distributed detection to identify the misbehavior of heterogeneous embedded IoT nodes efficiently and effectively in a closed-loop smart greenhouse farming system. To expand the monitoring space of a node, we exploited the Kalman-filter algorithm and simple statistical operations to obtain estimates of data. Accordingly, this enables a monitoring node to assess a target node that has distinct physical characteristics and access to natural phenomena. Along with this, we derive the behavior-rules that are specific to the target system and carefully translate these rules into a state machine diagram. Besides, we formally verify the functional correctness of the monitoring processes as well as ensure that the behavior specifications are completely covered by using the model checker tool UPPAAL. Through extensive experimental simulation using Proteus, we verify its applicability to resource-constrained embedded devices, e.g., Arduino-Uno, as well as show high accuracy in detecting misbehaving nodes while having low false alarms.

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 5G has introduced various emerging demands for new services and technologies that raised the bar for quality of service, latency, handovers, and data rates. Such diverse and perplexing network requirements bring numerous issues, among which security stands in the first row. The backhaul, which can be implemented as a wired or wireless solution, serves as a bridge between the radio access and core networks assuring connectivity to end users. The recent trends in backhaul usage rely on wireless technologies implemented using point-to-point (PTP) or point-to-multipoint (P2MP) configurations. Unfortunately, due to the nature of the transmission medium, the wireless backhaul is vulnerable and exposed to more various security threats and attacks than the wired one. In order to protect the backhaul, there have been several researches, whose authentication and key exchange scheme mainly depends on the existing security standards such as transport layer security (TLS), Internet Key Exchange version 1 (IKEv1), IKEv2, Host Identity Protocol (HIP), and Authentication and Key Agreement (AKA). However, such security standards cannot completely fulfil the security requirements including security policy update, key update, and balancing between security and efficiency, which are necessary for the emerging 5G networks. This is basically the motive behind why we study and propose a new security protocol for the backhaul link of wireless access network based on P2MP model. The proposed protocol is designed to be 5G-aware, and provides mutual authentication, perfect forward secrecy, confidentiality, integrity, secure key exchange, security policy update, key update, and balancing trade-off between efficiency and security while preventing resource exhaustion attacks. The protocol’s correctness is formally verified by the well-known formal security analysis tools: BAN-logic and Scyther. Moreover, the derived lemmas prove that the security requirements are satisfied. Finally, from a comparison analysis, it is shown that the proposed protocol is better than other standard protocols.

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.

Research to deal with distributed denial of service (DDoS) attacks was kicked off from long ago and has seen technological advancement along with an extensive 5G footprint. Prior studies, and still newer ones, in the realm of DDoS attacks in the 5G environment appear to be focused primarily on radio access network (RAN) and voice service network, meaning that there is no attempt to mitigate DDoS attacks targeted on core networks (CN) by applying artificial intelligence (AI) in modeling. In particular, such components of a CN as the Access and Mobility Management Function (AMF), Session Management Function (SMF), and User Plane Function (UPF), all being principal functions enabled to provide 5G services as base stations do, provide expansive connectivity with geographically very large area coverage that cannot be matched by the base stations. Moreover, to complete re-registration for one UE, required messages in protocols Packet Forwarding Control Protocol (PFCP) and HTTP/2 are approximately 40 in number. This implies that a DDoS attack targeting the CN has, once accomplished, a greater than expected impact, when compared to DDoS attacks targeting the RAN. Therefore, security mechanisms for the CN must be put into practice. This research proposes a method, along with a threat detection system, to mitigate signaling DDoS attacks targeted on 5G SA (standalone) CNs. It is verified that the use of fundamental ML classifiers together with preprocessing with entropy-based analysis (EBA) and statistics-based analysis (SBA) enables us to proactively react against signaling DDoS attacks. Additionally, the evaluation results manifest that the random forest achieves the best detection performance, with an average accuracy of 98.7%.

Searchable encryption enables the data owner to outsource their data to the cloud server while retaining the search ability. Recently, some researchers proposed a variant of searchable encryption, named single-writer/multi-reader searchable encryption (SMSE), in which any authorized data user can perform a search query. That is, each document identifier is encrypted using attribute-based encryption (ABE), such that an arbitrary authorized user whose attributes match the corresponding access policy can access the document. However, the cloud server cannot determine whether the user has the ability to decrypt the matched data. Thus, it has to response all the search results to the data user, which causes a heavy communication and computation cost. To cope with this problem, we present a novel SMSE scheme based on server-side match technique, where the cloud can filter the documents that cannot be decrypted by the user and only return the matched ones. In addition, the decryption is also efficient, independent with the access policy structure. Security and efficiency evaluation show that our proposed scheme can achieve the desired security goals, while dramatically reducing the communication and computation overhead.

With the development of the Internet and mobile technologies, the Internet of Things (IoT) era has arrived. Vehicle networking technology can not only facilitate people's travel but also effectively alleviate traffic congestion. The development of fog computing technology provides unlimited possibilities for the Internet of Vehicles (IoV). Intelligent navigator is a very important part of human-computer interaction in IoV. It carries a large number of tasks of recommending content for users. In order to get more accurate recommendation content, we propose a weighted interest degree recommendation algorithm using association rules for intelligence in the IoV. First, the user data are analyzed to establish the association rule mining algorithm. Second, the user interest score is predicted by analyzing the relevance between user interests to recommend personalized service for the user. From the simulation results, we can see that the proposed algorithm can achieve higher recommendation accuracy.

Aerial-terrestrial cloud networks (ATCNs), global integration of air and ground communication systems, pave a way for a large set of applications such as surveillance, on-demand transmissions, data-acquisition, and navigation. However, such networks suffer from crucial challenges of secure and reliable resource allocation and content-caching as the involved entities are highly dynamic and there is no fine-tuned strategy to accommodate their connectivity. To resolve this quandary, cog-chain, a novel paradigm for secure and reliable resource allocation and content-caching in ATCNs, is presented. Various requirements, key concepts, and issues with ATCNs are also presented along with basic concepts to establish a cog-chain in ATCNs. Feed and fetch modes are utilized depending on the involved entities and caching servers. In addition, a cog-chain communication protocol is presented which avails to evaluate the formation of a virtual cog-chain between the nodes and the content-caching servers. The efficacy of the proposed solution is demonstrated through consequential gains observed for signaling overheads, computational time, reliability, and resource allocation growth. The proposed approach operates with the signaling overheads ranging between 30.36 and 303.6 bytes*hops/sec and the formation time between 186 and 195 ms. Furthermore, the overall time consumption is 83.33% lower than the sequential-verification model and the resource allocation growth is 27.17% better than the sequential-verification model.

Vehicular social networks (VSNs) provide a variety of services for users based on social relationships through vehicular ad hoc networks (VANETs). During the communication in VSNs, vehicles are at risk of exposure to privacy information. Consequently, how to guarantee the security and privacy of vehicles is a critical issue. Ring signature is an effective mechanism to achieve anonymous authentication and communication. However, how to establish rings and how to select ring members become open problems. In this paper, a privacy-preserving scheme based on the pseudonym ring in VSNs is proposed. Hierarchical network architecture and trust model are established. A series of authentication protocols are then elaborated. According to the security and performance analysis, the proposed scheme is more robust and efficient compared with the typical ones.

Smart home networks have been recognized as one of its representative important applications in the forthcoming 5G era. It is also expected that in 5G networks, future smart home services will be much powered by mobility management, which enables users to remotely access and control their in-home Internet of Things (IoT) sensors and appliances anywhere anytime any device. As a major solution, Distributed IP Mobility Management (DMM) can be considered because it addresses the limitation of the centralized approaches as well as its flat architecture is suit for 5G networks. Obviously, without being protected, mobility management can cause smart home systems to be vulnerable to various security threats. Especially, it is of paramount important to protect data traffic transmitted between user mobile devices and their in-home IoT appliances because they include users' sensitive and critical privacy information. Taking this into consideration, it is necessary to support secure route optimization, which allows the involved devices to directly communicate each other in secure way while minimizing possibility of information leakage during data transmission. According to our best knowledge, there is no study on securing route optimization for DMM networks. Motivated by this, we propose a secure route optimization protocol for DMM-based smart home systems. The proposed security protocol, composed of the route optimization initialization and handover phases, is designed to provide mutual authentication, key exchange, perfect forward secrecy, and privacy protection. Its security is thoroughly verified through the two formal security analysis tools, BAN-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA). From the comparison analysis, it is shown that the proposed protocol is better than other standard protocols.

In recent years, the Internet of Things (IoT) has significantly increased the number of Internet connections to a large number of objects in different domains, including industry, homes and transportation. The LoRaWAN developed by the LoRa Alliance is a long-range wide area network specification suitable for an IoT environment due to its low-power communication. It provides a star-of-stars topology, well-defined MAC layer protocol and three communication modes to further lower its power consumption and employs the Advanced Encryption Standard (AES) cryptography and several session keys to increase its network security. However, for battery powered IoT end nodes, the AES encryption process consumes some amounts of power owing to involving multiple cycles of repetition. To solve this problem, in this study, we propose a low power consumed AES encryption architecture, named Low-Power AES Data Encryption Architecture (LPADA), which reduces the power consumed by the AES for data encryption by using low power SBox, power gating technique and power management method. A key updating procedure is also proposed to increase the security of the session-key renewal. The system is simulated using the Synopsys library with five different supply voltages. The experimental results show that 62.0% of dynamic power reduction and 88.5% of leakage power lowering have been achieved compared to the power consumed by traditional AES data encryption. The security analysis also shows that the key updating procedure for the LPADA enables mutual authentication between end nodes and application servers, and resists replay attacks and eavesdropping attacks from hackers.

Radio Frequency Identification (RFID) systems have been receiving much attention in the last few decades due to their effective role in our everyday life. They propose different solutions to many vital applications. Moreover, RFID systems are the backbone of modern Internet-of-Things (IoT) and Near-Field Communication (NFC) systems. Extending the capacity of such systems and making them more secure is the desired objective of the research community.

QoS-aware big data analysis is critical in Information-Centric Internet of Things (IC-IoT) system to support various applications like smart city, smart grid, smart health, intelligent transportation systems, and so on. The employment of non-volatile memory (NVM) in cloud or edge system provides good opportunity to improve quality of data analysis tasks. However, we have to face the data recovery problem led by NVM failure due to the limited write endurance. In this paper, we investigate the data recovery problem for QoS guarantee and system robustness, followed by proposing a rarity-aware data recovery algorithm. The core idea is to establish the rarity indicator to evaluate the replica distribution and service requirement comprehensively. With this idea, we give the lost replicas with distinguishing priority and eliminate the unnecessary replicas. Then, the data replicas are recovered stage by stage to guarantee QoS and provide system robustness. From our extensive experiments and simulations, it is shown that the proposed algorithm has significant performance improvement on QoS and robustness than the traditional direct data recovery method. Besides, the algorithm gives an acceptable data recovery time.

The identification of vulnerabilities in a mission-critical system is one of the challenges faced by a cyber-physical system (CPS). The incorporation of embedded Internet of Things (IoT) devices makes it tedious to identify vulnerability and difficult to control the service-interruptions and manage the operations losses. Rule-based mechanisms have been considered as a solution in the past. However, rule-based solutions operate on the goodwill of the generated rules and perform assumption-based detection. Such a solution often is far from the actual realization of the IoT runtime performance and can be fooled by zero-day attacks. Thus, this paper takes this issue as motivation and proposes better lightweight behavior rule specification-based misbehavior detection for the IoT-embedded cyber-physical systems (BRIoT). The key concept of our approach is to model a system with which misbehavior of an IoT device manifested as a result of attacks exploiting the vulnerability exposed may be detected through automatic model checking and formal verification, regardless of whether the attack is known or unknown. Automatic model checking and formal verification are achieved through a 2-layer Fuzzy-based hierarchical context-aware aspect-oriented Petri net (HCAPN) model, while effective misbehavior detection to avoid false alarms is achieved through a Barycentric-coordinated-based center of mass calculation method. The proposed approach is verified by an unmanned aerial vehicle (UAV) embedded in a UAV system. The feasibility of the proposed model is demonstrated with high reliability, low operational cost, low false-positives, low false-negatives, and high true positives in comparison with existing rule-based solutions.

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 STi is first selected, which can offer the best transmission performance for the secondary users to access the primary frequency spectrum. Then, another secondary transmitter STn, 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.

Vehicular network aims at providing intelligent transportation and ubiquitous network access. Edge computing is able to reduce the consumption of core network bandwidth and serving latency by processing the generated data at the network edge, and social network is able to provide precise services by analyzing user’s personal behaviors. In this paper, we propose a new network system referred to as vehicular social edge computing (VSEC) that inherits the advantages of both edge computing and social network. VSEC is capable of improving the drivers’ quality of experience while enhancing the service providers’ quality of service. In order to further improve the performance of VSEC, the network utility is modeled and maximized by optimally managing the available network resources via two steps. First, the total processing time is minimized to achieve the optimal payment of the user to each edge device for each kind of the required resource. Second, a utility model is proposed, and the available resources are optimally allocated based on the results from the first step. The two optimization problems are solved by the Lagrangian theory, and the closed-form expressions are obtained. Numerical simulations show different capacities in different scenarios, which may provide some useful insights for VSEC design.

Accessibility to industrial processes and direct obtaining of the desired services are the major facilities of Industrial Internet of Things (IIoT). IIoT covers crucial aspects of smart systems, such as automation, keenly intellective setups, asset management, and user-industry collaboration. These user-industry setups are facilitated by modern era network technologies, which also include an immense dependence on drones as one of the on-demand components for amending the quality and maximizing the coverage. However, these kinds of network formations require precise operations of drones and their perpetual assessment. The existing studies have highlighted these issues but fail to provide the behavior as well as the vulnerability evaluations of drones enabled IIoT. In addition, the existing studies are unable to provide statewise verification of drones and do not recognize anomaly drones based on their behavior over varying properties. Furthermore, the existing solutions lack facilities for including security policies which help in assessing the vulnerabilities with a higher accuracy. This paper fills this gap by using a novel N-layered hierarchical context-aware aspect-oriented Petri net model that not only evaluates the drone behavior but also assesses it for potential vulnerabilities by the utilization of security policies. Statewise verification is performed for the proposed model along with a simulation study, which designates its paramountcy in providing low-complex and low-overhead-based solution with a detection rate higher than 95% and accuracy as high as 99.9%. The proposed approach increases the probability of selecting a correct drone by 81.71% even in the case of a high number of failures.

The upcoming Fifth Generation (5G) networks can provide ultra-reliable ultra-low latency vehicle-to-everything for vehicular ad hoc networks (VANET) to promote road safety, traffic management, information dissemination, and automatic driving for drivers and passengers. However, 5G-VANET also attracts tremendous security and privacy concerns. Although several pseudonymous authentication schemes have been proposed for VANET, the expensive cost for their initial authentication may cause serious denial of service (DoS) attacks, which furthermore enables to do great harm to real space via VANET. Motivated by this, a puzzle-based co-authentication (PCA) scheme is proposed here. In the PCA scheme, the Hash puzzle is carefully designed to mitigate DoS attacks against the pseudonymous authentication process, which is facilitated through collaborative verification. The effectiveness and efficiency of the proposed scheme is approved by performance analysis based on theory and experimental results.

Internet-of-Things (IoT) systems are usually composed of thousands of different components among hardware devices and different software modules. In order to address the design of these complex systems, different abstraction layers are usually defined. However, as these layers are isolated, highlevel components always have uncertainty about the nature of the low-level components they relate with. In particular, as low-level component identities are not known by user applications, and current IoT systems are vulnerable to the injection of new components and to the modification of the behavior of existing ones (adequate security solutions at the network level for these problems have not been found yet), the reliability of the received data is generally compromised. In this context, new mechanisms are required to avoid the interactions or directly remove the malicious components relying on high-level information. This paper describes a statistical framework to discover IoT components with malicious behaviors, using a hybrid reputation model. On the one hand, an implicit reputation definition is employed, based on the observations made by a certain IoT component and other modules it relies on. On the other hand, an explicit reputation model considers a scheme of recommendations and negative grades. The proposed solution is evaluated in a simulation scenario by using the NS3 simulator, in order to perform an experimental validation.

Currently, Internet of Things (IoT) as an essential infrastructure proposed for industries and different applications has been popularly applied to different domains, such as healthcare and smart farming, for helping people to do something, aiming to improve our living environments. LoRaWAN, as a Long-Range Wide Area Network specification recommended by the LoRa Alliance, is a low power and long distance communication protocol suitable for IoT environments. This protocol adopts a widely used data encryption method, i.e., Advanced Encryption Standard (AES), developed based on powerful algebra operations and multiple encryption cycles to ensure its communication security. LoRaWAN reduces communication power by setting different transmission latencies for different end-devices; however, AES does not take into account its end device's encryption power. In this paper, a high secure but low-power consumption communication scheme for the LoRaWAN, named the Secure Low Power Communication (SeLPC) method, is proposed to further reduce end-devices' data encryption power by reducing encryption cycles of AES. In the SeLPC, encryption key and D-Box update procedure is presented to enhance security level and simplify the AES encryption process so that the power consumption can be further lowered. Comparing with the traditional AES, the analysis results show that the SeLPC can minimize the encryption power up to 26.2%. The SeLPC can also resist three attacks, including known-key, replay, and eavesdropping attacks and is practically helpful for use in LoRaWAN IoT environments.

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.

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

The communication in the Smart Home Internet of Things (SH-IoT) comprising various electronic devices and sensors is very sensitive and crucial. In addition, the key requirements of the SH-IoT include channel security, handover support, mobility management, and consistent data rates. Proxy mobile IPv6 (PMIPv6) is considered as one of the core solutions to handle extreme mobility; however, the default PMIPv6 cannot ensure performance enhancement in SH-IoT scenarios, i.e., Route Optimization (RO). The existing security protocols for PMIPv6 cannot support secure RO for smart home IoT services, where mobile nodes (MNs) communicate with home IoT devices not belonging to their domain. Motivated by this, a secure protocol is proposed, which uses trust between PMIPv6 domain and smart home to ensure security as well as performance over the path between MNs and home IoT devices. The proposed protocol includes steps for secure RO and handover management, where mutual authentication, key exchange, perfect forward secrecy, and privacy are supported. The correctness of the proposed protocol is formally analyzed using BAN-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA). Furthermore, network simulations are conducted to evaluate the performance efficiency of the proposed protocol. The results show that the proposed approach is capable of providing secure transmission by resolving the RO problem in PMIPv6 along with the reduction in handover latency, end to end delay and packet loss, and enhancement in throughput and transmission rate even during the handover phase.

In the recent years, the user activities over online social networks (OSNs) have increased tremendously. A large number of users share information across the different social networking platforms. The information across the OSNs is easy to access, and thus, can be easily used by the fraudulent users for misleading the entire community. Such fraudulent users are termed anomalies. In this paper, a problem of cross-platform anomalies is considered, which possesses different behaviors by an individual with different users across the multiple OSNs. The variation in the behavior and activity makes it difficult to identify such anomalies. A solution to this problem is proposed on the basis of cognitive tokens, which provide an intelligent sensing model for anomalies detection (ISMA) by deliberately inducing faulty data to attract the anomalous users. A common login system for different OSNs is also suggested as a part of collaborative anomaly identification across different OSNs. A fair play point approach is used for the determination of anomalies. Both simulations and email-based real data sets are used to measure the performance of the proposed approach. Furthermore, as an example of implementation, a case study is presented for anomaly detection in Internet of Things. The proposed approach is able to provide the highest accuracy at the rate of 99.2%; this is 25.1% higher as compared with the SVM-RBF and sigmoid approach, and 22% higher than that of the k-nearest neighbor approach. Furthermore, the proposed ISMA also caused less error in detecting the anomalies, which were within the range of 0.1% to 2.8%. The error in identification is reduced up to 96.6% in comparison with the SVM and k-nearest neighbor approaches. The gains in comparative results validate the efficiency of the ISMA in identification and classification of anomalies in cross-platform OSNs.

The multi-homed mobile devices in the mobile cloud computing (MCC) systems can improve their throughput by allocating the application data over several paths simultaneously, enabled by the promising Multipath TCP (MPTCP) technology. Meanwhile, network attacks against current Internet infrastructures are likely to increase, especially with the widely deployment of MCC systems. When a MPTCP connection is under network attacks and becomes a poor-performing path or a broken path, it can significantly affect other stable paths and in the absence of related schemes to handle this, MPTCP will undoubtedly suffer from serious performance degradation. Moreover, applying MPTCP to cloud data delivery may generally lead to higher energy consumption and is not favorable to a power-constrained mobile device. In this paper, we propose MPTCP-La/E2, a low-rate distributed denial-of-service (LDDoS) attack-aware energy-efficient MPTCP solution aiming at: 1) avoiding the LDDoS-caused performance degradation of cloud multipath transmission, which has been seldom considered in existing MPTCP solutions and 2) optimizing the energy usage while still maintaining user's perceived quality of cloud multipathing services. The simulation results show that MPTCP-La/E2 outperforms the baseline MPTCP in terms of QoS and energy-savings in a multihomed MCC network environment.

End-to-end transmission is an essential pattern in current Internet. It had brought obvious conveniences and potential hazard simultaneously. New network technology provides plenty of opportunities to avoid the harmful influences. After reviewing the identifier-based network, a candidate solution of future network, we proposed a smart collaborative connection management scheme for improving the dependability. Comparing with the conventional approaches, our scheme can protect the hosts via intermediate network equipments. During transmission process, the current usage pattern will not be influenced. Traditional unicast can be easily and smoothly enabled. The details of the scheme design are presented via four perspectives of connection identifier, i.e., communication mode, generation, distribution, and deployment. Then the supporting approaches of multipath transport, service migration, network mobility, P2P distribution, and application layer multicast are carefully investigated. The results of implementation and validation illustrate that the performances of new scheme are acceptable. We expect that this paper could provide a compatible and effective solution for both current and future Internet.

Pervasive social networking (PSN) aims at bridging the gap between the services and users by providing a platform for social communication irrespective of the time and location. With the advent of a new era of high-speed telecommunication services, mobile users have evolved to a large extent demanding secure, private, and trustworthy services. Online social networks have evolved as pervasive online social networks (POSNs), which uses a common platform to connect users from hybrid applications. Trust has always been a concern for these networks. However, existing approaches tend to provide application-specific trust management, thus resulting in the cost of excessive network resource utilization and high computations. In this paper, a pervasive trust management framework is presented for POSNs, which is capable of generating high trust value between the users with a lower cost of monitoring. The proposed approach uses a flexible mixture model to develop the system around six different properties, and then utilizes the concept of osmotic computing to perform computational offloading, which reduces the number of computations as well as computational time. The novel concepts of lock door policy and intermediate state management procedure are used to allow trust visualization by providing efficient identification of trustworthy and untrustworthy users. The proposed approach is capable of predicting user ratings efficiently with extremely low errors, which are in the range of ±2. The effectiveness of the proposed approach is demonstrated using theoretical and numerical analyses along with data set-based simulations.

Software-defined satellite network (SDSN) is a novel framework, which brings softwaredefined network technologies in the satellite networks. It has great potential to achieve effective and flexible management in the satellite networks. There are two burning issues to be solved for the flow table management in SDSN. First, the ternary content addressable memory (TCAM) space is limited on satellites and the flow table size should be reduced. Second, the frequent handovers will lead to an increase in the flow table size in SDSN. Due to the limited flow table space, a lot of flows will be dropped if the flow table is full during the handover. To address these issues, we first give a description of our focused flow table management problems. Then, we propose SAT-FLOW, a multi-strategy flow table management method for SDSN. SAT-FLOW considers three key points, limited TCAM space, classified traffic, and handover. SAT-FLOW contains two heuristic algorithms, named dynamic classified timeout (DCT) algorithm and timeout strategybased mobility management (TSMM) algorithm. DCT aims to reduce the flow table size and TSMM aims to reduce the drop flows during the handover. We implement SAT-FLOW and conduct contrast experiments. The experimental results verify the good performance in terms of transmission quality, idle_timeout values distribution, a 15.27%-24.34% decrease in flow table size, an 8.2%-10.4% decrease in drop-flow rate, and a 4.92%-5.7% decrease in table misses for the high priority traffic during the handover.

With the popularity of cloud computing and high performance computing, the size and the amount of the datacenter develop rapidly, which also causes the serious challenges on energy consumption. Dynamic voltage and frequency scaling (DVFS) is an effective technique for energy saving. Many previous works addressed energy-officiate task scheduling based on DVFS. However, these works need to know the total workload (execution time) of tasks, which is difficult for some real-time tasks requests. In this paper, we propose a new task model that describes the QoS requirements of tasks with the minimum frequency. In addition, we define energy consumption ratio (ECR) to evaluate the efficiency of different frequencies under which to execute a take. Thus, it is possible to convert the energy-efficient task scheduling problem into minimizing the total ECR. By transforming the problem to the variable size bin packing, we prove that the minimization of ECR is NP-hard in this paper. Because of the difficulty of this problem, we propose task allocation and scheduling methods based on the feature of this problem. The proposed methods dispatch the coming tasks to the active servers by using servers as less as possible and adjust the execution frequencies of relative cores to save energy. When a task is finished, we propose a processor-level migration algorithm to reschedule remaining tasks among processors on an individual server and dynamically balance the workloads and lower the total ECR on this server. The experiments in the real test-bed system and simulation show that our strategy outperforms other ones, which verifies the good performance of our strategy on energy saving.

The Internet of Things (IoT) has emerged as an important field of future network research that focuses on ubiquitous service patterns and universal access for people, devices, processes, etc. Motivated by great market demand and potential profit, future innovations for the application and accommodation for IoT will soon be realized and deployed expeditiously. However, those innovations will inevitably expose IoTs to security and privacy risks. Our society has witnessed tremendous concerns about these issues. Therefore, examining IoT applications and services closely for security and privacy vulnerabilities is of the utmost importance and can be further realized in three dimensions, namely, network architectural principles, application design, and high quality of service. From small atomic elements of the network to entire service packages, fine-grained verification should be performed to reduce security and privacy risks of an individual or the entire network.

Space-air-ground integrated Internet of things can improve the scope of Internet of things applications significantly by offering truly global coverage all over the world. While space-air-ground integrated Internet of things is promising to be very useful in many aspects, its deployment and application should overcome severe security threats, for example, interceptions, identity forgery, data tampering, and so on. Authentication is an essential step to protect the Internet of things security, and mutual authentication (i.e. two-way authentication) is especially important to ensure the security of both communication parties simultaneously. However, the intrinsical properties of network dynamics and wide coverage make the authentication concern in space-air-ground integrated Internet of things extremely challenging than traditional Internet of things networks. In this article, we propose MASIT, an identity-based efficient and lightweight mutual authentication scheme for space-air-ground integrated Internet of things. MASIT exploits the natural broadcast property of space-air-ground integrated Internet of things to speed up authentication process, and leverage the distinguished feature of IPv6 to support concurrent numerous nodes. Theoretically, we prove that MASIT is existential unforgeable secure under adaptively chosen message and identity Attacks. We also implement MASIT and other existing typical identity-based encryption schemes and evaluate their performance in real platforms. Experimental results showed that, MASIT outperforms the existing identity-based encryption schemes significantly, that is, the signature verification time can be reduced by 50% to 60%, and the user signature size can be reduced by 13% to 50%.