The rapid growth of IoT devices introduces serious security risks, compounded by the heterogeneity of firmware across architectures, compilers, and optimization settings. Existing vulnerability detection methods struggle with efficiency, cross-platform generalization, and adversarial robustness. We present LEGEM, a framework that integrates large language models (LLMs) with graph embedding to address these challenges. LEGEM introduces two key framework components: (1) a multi-modal dataset construction framework (MDCF) that unifies and standardizes data processing and adversarial example generation, and (2) an adaptive training-validation loop (ATVL) that integrates hyperparameter optimization with adversarial validation to enhance both efficiency and robustness. By incorporating 96,797 LLM-generated adversarial samples with ensemble learning, LEGEM improves precision and reduces false positives under attack. Experimental results show LEGEM achieves higher accuracy (ROC-AUC 0.973 vs. 0.970 for GESS) and substantially faster matching (28.3% faster than GESS and 82.1% than Gemini). Cross-dataset evaluation on real IoT firmware further demonstrates strong generalization (AUC 0.998). Overall, LEGEM delivers a practical and robust solution for IoT binary analysis, advancing detection efficiency and resilience compared to existing methods.

Industrial Internet of Things (IIoT) faces increasing security risks with wide applications. Compared with the Internet, the IIoT has a broader attack surface and unique structural characteristics, posing difficulties in directly transferring the previous vulnerability analysis techniques. This paper proposes a triple-stage vulnerability analysis framework (TS-VulA) for IIoT via attack graphs combining ModernBERT and multi-layer heterogeneous networks, which includes three stages. In the first stage, the SentenceBERT based on ModernBERT and IIoT disruption losses are combined to conduct a single-node vulnerability assessment from the aspects of likelihood and criticality of vulnerability exploitation. In the second stage, an IIoT device importance calculation based on multi-layer heterogeneous network theory is proposed, which can acquire the inherent relationships among various IIoT devices. Stage 3 extends attack graph rules for IIoT, then computes node priorities by integrating vulnerability assessment and device importance, which can guide the mitigation strategy. Extensive experiments demonstrate that the proposed vulnerability assessment method achieves an average accuracy and average precision of 87.86% and 87.33%, both exceeding the existing methods. Simulated case studies illustrate that the proposed TS-VulA outperforms the prevailing vulnerability analysis methods.

Previous schemes for designing secure branch prediction unit (SBPU) based on physical isolation can only offer limited security and significantly affect BPU’s prediction capability, leading to prominent performance degradation. Moreover, encryption-based SBPU schemes based on periodic key rerandomization have the risk of being compromised by advanced attack algorithms, and the performance overhead is also considerable. To this end, this paper proposes conflict-invisible SBPU (CIBPU). CIBPU employs redundant storage design, load-aware indexing, and replacement design, as well as an encryption mechanism without requiring periodic key updates, to prevent attackers’ perception of branch conflicts. We provide a thorough security analysis, which shows that CIBPU achieves strong security throughout the BPU’s lifecycle. We implement CIBPU in a RISC-V core model in gem5. The experimental results show that CIBPU causes an average performance overhead of only 2.9%–4.0% with acceptable hardware storage overhead, which is the lowest among the state-of-the-art SBPU schemes. CIBPU has also been implemented in the open-source RISC-V core, SonicBOOM, which is then burned onto an FPGA board. The evaluation based on the board shows an average performance degradation of 2.01%, which is approximately consistent with the result obtained in gem5.

Transient execution attacks can exploit speculative execution to leak sensitive information through cache systems. Consequently, numerous cache-centric secure speculation defenses have been proposed. However, as we demonstrate both theoretically and empirically, these defenses remain vulnerable to data leakage because they neglect or fail to fully address the security issues posed by the hardware prefetcher, a critical component of modern cache systems, within the speculative execution path. In this work, we develop a new attack framework, SpectrePrefetch, which exploits hardware prefetchers as transmission mediums for leaking secrets during speculative execution. Specifically, we introduce two variants of SpectrePrefetch attacks that encode transient secrets into the prefetching patterns and prefetching confidence, respectively, and then recover secrets by probing the cache state or the prefetcher state. We launch SpectrePrefetch to attack several Intel CPUs and a Gem5 simulator, demonstrating its feasibility, robustness, bandwidth, scalability, and security implications on existing defenses. The results show that SpectrePrefetch can leak secrets at a high rate of 31.25 Kbps with an accuracy of 98.87%. More seriously, SpectrePrefetch undermines cache-centric secure speculation defenses or even secure cache designs, and is challenging to mitigate. Our experimental results show that simply restricting the prefetcher to update only on committed instructions, as proposed in MuonTrap, nearly loses all performance benefits provided by hardware prefetching. Finally, we propose a low-cost, scalable non-deterministic prefetching defense against transient execution attacks exploiting hardware prefetchers, while maintaining or even improving average performance on SPEC2017 benchmarks.

With the booming development of the underwater Internet of Things, underwater devices and data volume are explosively increasing. Thanks to the advantages of high efficiency and low complexity, the emerging rate splitting multiple access (RSMA) can be integrated with underwater optical wireless communication (UOWC), which can help improve resource utilization and system performance. However, the oceanic characteristics are distinctive and pose great challenges for RSMA-enabled UOWC networks. Precoding design is crucial for benefitting from RSMA-enabled systems, and thus, we explore the precoding optimization for RSMA-enabled UOWC networks over underwater turbulent channels. Specifically, the RSMA-enabled UOWC network is modeled, where a combined turbulent channel involving the effects of absorption, scattering, and underwater turbulence is considered, and a message transmission scheme with rate splitting design is analyzed. A precoding problem is formulated to maximize the ergodic sum rate (ESR) under the constraint of total transmitted power. Since the accurate expression of ESR is a non-closed form and intractable, we first propose a consecutive Fenton-Wilkinson (CFW)-based ergodic rate approximation approach to solve this formulation problem. Fenton-Wilkinson moment matching is consecutively employed to achieve a closed-form and low-complexity expression of the ergodic rate, as only Fenton-Wilkinson moment matching method can offer closed-form solutions for underlying parameters of the approximating log-normal distributions. For further solving the non-convex precoding problem, the successive convex approximation (SCA) approach is adopted, which is particularly applicable for resource-limited underwater environments. The effectiveness of the proposed CFW approach and precoding strategy is evaluated by extensive simulation results for various user deployments and different network loads.

A compiler converts smart contract source code into bytecode, ensuring behavior consistency between them. However, as compiler is also a program, it may contain bugs that disrupt this consistency, known as Compiler-Introduced Bugs (CIBs). Of the latest 4,857 verified smart contracts coded in Solidity, approximately 58% still use compilers that contain at least one CIB. These CIBs can be exploited by attackers to bypass security checks or inject malicious data, leading to significant security issues, which becomes even more serious for smart contracts in blockchain as they cannot be modified after being deployed. To this end, this paper proposes sBugChecker, to the best of our knowledge, the first systematic framework designed to automatically and effectively detect CIBs for smart contracts coded in Solidity. sBugChecker can be readily extended with the rule customization suite we propose based on domain specific language. Additionally, it employs two static analytical methods, i.e., pattern matching, and symbolic execution, to identify CIBs’ triggering conditions and confirm their impacts, broadening its detection scope and improving its detection efficiency. To evaluate sBugChecker’s performance, we construct a CIB mutated smart contract dataset, which is the first publicly-available one for this study. According to the evaluation based on this dataset, sBugChecker performs exceptionally well, with detection precision, recall, and F-measure on average achieving 96.6%, 95.5% and 96.0%, respectively. Moreover, sBugChecker has been applied to successfully discover real-world deployed smart contracts capable of triggering CIBs.

Recently intrusion detection technology has been deployed in the Industrial Internet of Things (IIoT), which is an efficacious approach to enhancing security. However, identifying previously unseen and unknown attacks, referred to as the open-set problem, has become increasingly difficult due to the openness of IoT architecture and the continuous evolution of attack patterns. Moreover, existing open-set intrusion detection solutions are challenging to be applied directly to IIoT because of their unique characteristics, such as limited computational and storage capabilities, long detection times, and the inability to continuously learn. In this paper, we propose an efficient, lightweight, and dynamic open-set intrusion detection scheme for IIoT. It consists of three stages: the known attack classification stage focuses on extracting features from known data to efficiently classify normal data and known attacks; the unknown attack recognition stage analyzes the distribution of reconstruction errors to effectively distinguish between known data and unknown attacks; and the dynamic update detection stage introduces a lightweight detection architecture for unknown attacks detection, significantly reducing the computational overhead and storage requirements of IIoT devices. Simultaneously, it learns from and updates with newly detected unknown attacks to further optimize detection capabilities. We conduct experiments on four widely used datasets to evaluate the performance of open-set intrusion detection for IIoT. The experimental results delineate the superiority of our proposed method over four state-of-the-art approaches in open-set intrusion detection. Meanwhile, our proposed lightweight model updating method significantly reduces detection time by over 65% and memory overhead by over 80% compared to retraining methods, while achieving an average detection accuracy of 96%.

With the explosive development of mobile devices, mobile crowdsensing (MCS) has emerged as a promising approach for large-scale sensing data collection. In the research of MCS, blockchain technology has been widely adopted to decentralize the traditional mobile crowdsensing and tackle the problem of single point of failure. Incentive mechanisms are devised to boost participation with fairness and truthfulness. However, to better determine the incentive strategy, participants’ privacy can be disclosed on top of the blockchain and obtained by adversaries during the transmission and execution of user data, leading to serious security issues. In this paper, we propose a two-stage incentive scheme with efficiency, robustness and privacy preservation considered based on the combination of blockchain technology and Trusted Execution Environment (TEE). Detailedly, we design two kinds of smart contracts, where on-chain public contracts support the procedure of general crowdsensing interactions, and off-chain private ones enabled by TEE complete the privacy-preserving computations, including an online incentive mechanism for worker recruitment decisions and a truth discovery algorithm for data aggregation. Recovery mechanism and hash check mechanism are introduced to avoid TEE provider failures and TEE providers’ attacks, respectively. Our scheme is proved to be theoretically secure in terms of private information protection, worker participation anonymity, and data aggregation privacy. Experimental results also verify the feasibility and superiority of our incentive scheme.

With the explosive development of mobile devices, mobile crowdsensing (MCS) has emerged as a promising approach for large-scale sensing data collection, while blockchain has been introduced to secure MCS. This research proposes a two-stage incentive scheme with efficiency, robustness and privacy preservation based on the combination of blockchain technology and Trusted Execution Environment (TEE). Detailedly, we design two kinds of smart contracts where on-chain public ones support the procedure of general crowdsensing interactions, and off-chain private contracts enabled by TEE completes the privacy-preserving computations, including an online incentive mechanism for worker recruitment decision and a truth discovery algorithm for data aggregation. Diverse mechanisms are introduced to avoid attacks and failures of TEE providers. Experimental results also verify the feasibility and superiority of our scheme.

With the explosive growth of Internet of Things (IoT) devices, the IPv6 routing protocol for low-power and lossy networks (RPL) has been widely studied and applied. However, due to lack of complete security mechanisms, it faces many threats, one of which is wormhole attack. Wormhole attack could mislead network traffic flow, cause network congestion and increase packet delivery latency. Besides, it can also be combined with the other types of attacks to become more threatening. In this article, we propose two schemes, called routing loop detection for wormhole and routing loop detection for wormhole combined with greyhole, to detect wormhole nodes for different attack modes in RPL-based networks. Our methods could not only detect but also localize the adversaries more effectively in comparison with the state-of-the-art scheme, LiDL. We have completely implemented the proposed two schemes, as well as LiDL, in the Contiki IoT operating system and made an extensive comparison among them based on the Sky mote. The obtained results demonstrate the feasibility and superiority of our schemes in terms of detection speed, accuracy and network performance improvement.

Distributed Denial of Service (DDoS) attacks threaten cloud servers by flooding redundant requests, leading to system resource exhaustion and legitimate service shutdown. Existing DDoS attack mitigation mechanisms mainly rely on resource expansion, which may result in unexpected resource over-provisioning and accordingly increase cloud system costs. To effectively mitigate DDoS attacks without consuming extra resources, the main challenges lie in the compromisesbetween incoming requests and available cloud resources. This paper proposes a resource-aware DDoS attack mitigation framework named RAM, where the mechanism of feedback in control theory is employed to adaptively adjust the interaction between incoming requests and available cloud resources. Specifically, two indicators including request confidence level and maximum cloud workload are designed. In terms of these two indicators, the incoming requests will be classified using proportional-integral-derivative (PID) feedback control-based classification scheme with request determination adaptation. The incoming requests can be subsequently processed according to their confidence levels as well as the workload and available resources of cloud servers, which achieves an effective resource-aware mitigation of DDoS attacks. Extensive experiments have been conducted to verify the effectiveness of RAM, which demonstrate that the proposed RAM can improve the request classification performance and guarantee the quality of service.

Mobile crowdsensing (MCS) is an efficient approach for large-scale sensing data collection by leveraging the mobility and capability of mobile devices. To avoid the weaknesses of traditional centralized crowdsensing systems, blockchain has been introduced to secure the process of MCS. This paper studies a location-aware scenario, where privacy of users are protected in a blockchain- based MCS system, and formulates an optimization problem to maximize the coverage given a budget based on reverse auction. An incentive mechanism named MMCB is further proposed and implemented as smart contracts in blockchain to solve the problem. We demonstrate that the mechanism achieves a set of desirable properties, including computation efficiency, individual rationality, truthfulness, budget feasibility, approximation, and privacy preservation. To protect the identity privacy of workers and obtain anonymity, a linkable ring signature is employed in smart contracts. In addition, a Pedersen commitment is utilized for protecting workers’ bid profile and the submitted sensing data is encrypted and only accessible to the requester. We implement a prototype system based on the Hyperledger Fabric platform, and the evaluation results show that our privacy-preserving incentive mechanism architecture improves 36.2% coverage and reduces 53.1% payment with better security level compared to the state-of-the-art schemes.

It is necessary for rescuers to localize a target trapped in a an unknown area resulting from various natural disasters or human warfare. The global navigation satellite systems and existing wireless and cellular infrastructures may have been partially or totally constrained and not available for localization in the target area. In this article, we propose a simple yet effective single-anchor mobile localization scheme, called TSAL as a potential solution in the case where traditional localization methods fail. By building three Cartesian coordinate systems and carrying out distance and/or steering-angle measurement with the off-the-shelf approaches while the target node is moving, utilizing just one of existing normally-functioning cellular Base Stations (BSs) as the only anchor or redeploying only one BS is enough to localize the target. In addition, TSAL also works with multiple anchor nodes and we propose a corresponding scheme based on TSAL, called TML, which can obtain more accurate localization. Theoretical analyses, extensive simulations and real-world experiments are conducted for evaluating the proposed schemes, with the effects of a set of parameter settings investigated, which shows the high availability and effectiveness of our schemes.

In recent years, mobile crowdsensing (MCS) has been widely adopted as an efficient method for large-scale data collection. In MCS systems, insufficient participation and unstable data quality have become two crucial issues that prevent crowdsensing from further development. Thus designing a valid incentive mechanism is essentially significant. Most of the existing works on incentive mechanism design focus on single-objective optimization with various constraints. However, in the real-world crowdsensing, it is common that several objectives to be optimized exist. Furthermore, constraints on budget or cost are often seen in MCS systems as the feasibility of implementing incentive mechanism is indispensable. This paper studies a bi-objective optimization scenario of MCS to simultaneously optimize total value function and coverage function with budget/cost constraint through a set of problem transformations. Then a budget- or cost-feasible bi-objective incentive mechanism is further proposed to solve the aforementioned bi-objective optimization problem through the combination of binary search and greedy heuristic solution under budget or cost constraint, respectively. Through both rigorous theoretical analysis and extensive simulations, the obtained results demonstrate that the mechanisms achieve computation efficiency, individual rationality, truthfulness, and budget or cost feasibility, while one mechanism obtains an approximation.

Privacy-preserving localization plays a crucial role in enabling various applications on the Internet of Things (IoT). Existing work applies random zero-sum noise to develop privacy-preserving localization, which achieves efficiency and accuracy by adding random noise to preserve private information and cancelling the effect of the noise with the zero-sum characteristic, respectively. However, in practice, some nodes in IoT scenarios may misbehave, not following a pre-defined protocol but adding false noise or tampering with information, which leads to the trust issue for privacy-preserving localization. In this paper, we integrate blockchains with zero-sum noise to achieve trusted privacy-preserving localization against misbehaving nodes. Specifically, a three-layer framework is designed by combining private blockchains with a zero-sum noise-adding mechanism. In the sensing layer, nodes are divided into groups to perform the first noise-adding process to preserve their private location information during location aggregation inside the group. In the blockchain layer, each group constructs the private blockchains to achieve trustworthiness without increasing the risk of privacy leakage and performs the second noise-adding process to protect intermediate information. In the application layer, the target node aggregates the intermediate information from each group to estimate its location. Then, under the framework, we propose an Enhanced Privacy-Preserving Localization (EPPL) algorithm to securely calculate the location of the target node against misbehaving nodes. The correctness, accuracy, privacy, trustworthiness, and efficiency of EPPL are analyzed. The performance of EPPL is evaluated by using simulations. Compared with existing random noise-based methods, it is shown that EPPL can effectively enhance privacy-preserving localization.

Low Earth Orbit (LEO) satellite-based edge computing offers an innovative paradigm for offloading the computing tasks of resource-limited User Equipments (UEs) in the environment lacking terrestrial networks. By offloading a portion of the computing tasks to satellite-mounted servers in space, it can significantly reduce the energy consumption of the UEs while reducing the delay. However, most existing works focus on the performance and energy consumption from the system point of view, neglecting the dynamic Quality of Experience (QoE) of the UEs: the UEs tend to place a higher value on saving energy when their remaining battery level is low, while seeking a lower delay when the battery power is sufficient. To this end, we build a QoE-aware task offloading model in space-terrestrial integrated networks where the UEs can adjust the preference on the delay and energy consumption based on their remaining battery level. We then propose a Self-Adaptive Multi-Agent Deep Deterministic Policy Gradient (SA-MADDPG) task offloading scheme based on Deep Reinforcement Learning (DRL). SA-MADDPG learns the status of the satellite-mounted edge servers and makes different offloading decisions at different battery levels. When the battery level is high, UEs will be more likely to perform computation locally to reduce the delay; on the contrary, when the UE battery level is low, they will seek to offload the computation tasks to the satellites to save energy. Through simulation experiments, we demonstrate that SA-MADDPG can dynamically adjust the UE offloading decision based on their current battery level and effectively improve the user QoE.

Multidomain Internet of Things (IoT) is faced with serious domain interoperability (DI) and compatibility issues since different intradomain authorization and authentication (A&A) mechanisms are deployed without the consideration of interdomain A&A. This article proposes a blockchain-assisted scheme to achieve flexible intra- and inter-domain A&A simultaneously and seamlessly. Specifically, we first design a contract-based mutual access control agreement on top of a consortium blockchain, where domain managers can manage their access permission without any trusted parties. Based on the agreement, a secure and privacy-preserving authentication protocol is further proposed by tailoring one-out-of-many proof techniques, which enables IoT devices to anonymously access authorized IoT domains. We additionally design a voting-based protocol by using a threshold-based cryptosystem. The protocol allows domain managers to transparently audit resource access with the assistance of the blockchain. Detailed security analysis demonstrates that the proposed scheme achieves the security properties, such as DI, privacy protection, and accountability. Finally, we develop two proof-of-concept prototypes in a physical testbed and virtual machine, respectively, based on an open-source blockchain platform to show our scheme’s efficiency in terms of computation and communication overhead.

The growing power consumption of heterogeneous clusters has attracted great interests from both academia and industry. Although there have been extensive studies on energy-efficient task scheduling algorithms to address this problem, most of them are task-centric. However, as a task is fine-grained enough, the scheduling algorithm can put more efforts on utilizing different power characteristics of heterogeneous servers, which has not been fully explored. This paper presents a Linear Programming-based Energy-efficient Load Balancing (LP-ELB) scheme for heteroge-neous clusters. The power model of each sever in the cluster is obtained in advance, and the overall optimization problem is formulated as a mixed integer linear programming problem. LP-ELB tackles this problem through a two-phase heuristic: a server selection phase for choosing a minimal set of servers, and a load partition phase for calculating load distribution. An optimal solution to the relaxed problem in the second phase can be provided finally. Experiments on both simulated and real-world testbeds validate the effectiveness of the proposed method, and reveal that LP-ELB is superior to competitor algorithms, such as Round-Robin and power-aware Weighted Round-Robin.

Public Key Infrastructure (PKI) is the cornerstone technology to solve trust issues in cyberspace. However, PKI faces a serious problem of centralized trust in Certificate Authorities (CAs). Fraudulent certificates issued by CAs due to misoperation, being deceived, or being compromised, are used to launch attacks like Man-in-the-Middle (MitM), spoofing, etc. To enhance the security of CAs, we present a domain-centric system based on blockchain called Domain Transparency (DT). Domain owners are enabled to declare issuance policies that CAs should comply with in the DT system, so that all issued certificates are authorized by them. Furthermore, we design a Domain Configuration Transaction (DCT) to manage policies and certificates of domains. To resist CAs’ misbehaviors, domain owners are involved in the certificate issuance process to balance the absolute authority of CAs. We conduct extensive security analysis and implement a prototype of DT based on Hyperledger Fabric for performance evaluations. Experimental results reveal that DT is superior to competitor schemes in terms of functionality, storage and communication cost.

Modern processors make use of optimization techniques such as cache and speculation mechanisms to greatly improve performance. But recent research has found that these techniques can also be exploited by attackers to perform powerful side-channel attacks. A large number of powerful cache-based attacks have been replicated and enhanced over Intel X86- and ARM-based architectures, but there is a relative lack of research on RISC-V-based architectures. Xuantie-910 and BOOM are both RISC-V-based processors. So far, cache-side channels in the unprivileged case of Xuantie-910 have not been proven, while cache attacks against BOOM are proliferating. There are two types of caches, including physically-indexed physically-tagged (PIPT) cache (adopted by Xuantie-910) and virtually-indexed physically-tagged (VIPT) cache (adopted by BOOM), corresponding to two different cache addressing forms. VIPT has higher addressing performance than PIPT, since it can directly obtain cache line index from virtual address. In this paper, we study Xuantie-910 and BOOM to explore the impact of cache design on the security of RISC-V-based microarchitecture. Specifically, we compare the impact of their cache addressing forms on precise flushing of cache lines at specified locations, which plays an important role in cache side-channel attacks. Experimental results show that for the VIPT cache in BOOM, the location-specified cache lines can be accurately flushed, and Spectre attack can be successfully carried out by using the cache side-channel. On the other hand, for the PIPT cache in Xuantie-910, it is impossible for attackers to directly and accurately flush the specified location of cache without affecting performance, which hinders the success of cache side-channel attacks. This provides us with an insight that one can adopt a VIPT-based cache with a mechanism similar to PIPT for preventing the accurate access of cache line index, which can not only keep the advantage of high-performance addressing in VIPT but also improve chip security.

Abstract Clustering and layering are widely employed in large‐scale wireless sensor networks (WSNs) to alleviate data implosion, reduce transmission delay and improve energy efficiency. It is vital to conduct performance analysis in clustered WSNs for better defining and designing networks. Here, a general analytical framework based on a randomly spacial model is proposed to conduct inter‐cluster interference analysis and physical‐layer security analysis for clustered WSNs. It is assumed that two adjacent cluster heads exchange confidential massage with a passive eavesdropper and the non‐head nodes in clusters could be selected as cooperative jamming (CJ) nodes to disturb the eavesdropper. All mentioned nodes are randomly located in their specific areas. Under the above settings, three scenarios, namely interference, eavesdropping and CJ are considered, and a stochastic geometry tool based on kinematic measure is employed for analysis. Comparing to existing stochastic geometry methods, the proposed analytical framework can handle arbitrarily shaped, disjoint and tiered networks. The results obtained from extensive simulations have verified the rationality of the framework and demonstrated the impact of different parameters on the performance metrics of interest. The comparison with PPP also shows the advantages of this proposed model.

Internet of Things (IoT) has been ubiquitous in both industrial and living areas, but also known for its weak security. Being as the first defense line against various cyberattacks, authentication is even more critical to IoT applications. Moreover, there has been a growing demand for cross-domain collaboration, leading to an increasing need for cross-domain authentication. Recently, certificate-based authentication schemes have been extensively studied. However, many of these schemes are not efficient in computation, storage, and communication, which are highly required in IoT. In this paper, we propose a lightweight authentication scheme based on consortium blockchain and design a cryptocurrency-like digital token to build trust. Furthermore, trust lifecycle management is performed by manipulating the amount of tokens. The comprehensive analysis and evaluation demonstrate that the proposed scheme is resistant to various common attacks and more efficient than competitor schemes in terms of storage, communication, and authentication cost.

The increasing diversity of Internet-of-Things (IoT) application scenarios and explosive growth of access devices have brought more frequent exchanges of resources between different administrative domains. Cross-domain authentication has become a key to safeguard communication and resource interaction among domains. Traditional centralized authentication schemes present heavy management overhead and trust challenges in cross-domain scenarios. Most of existing studies are incapable of establishing trust relationships between domains deployed with different authentication schemes, rendering such high-cost schemes difficult to be generalized. Further, the cross-domain scenario of IoT also raises additional requirements for device privacy and system overhead. In order to tackle these issues, this paper proposes a complete cross-domain authentication and privacy protection scheme, called CCAP, for the IoT based on consortium blockchain. CCAP achieves cross-domain authentication among the IoT domains which may have different configurations from each other. Further, CCAP can be cost-effectively deployed in resource-limited IoT domains and can offer privacy protection and efficient and secure communication for IoT devices. We demonstrate the effectiveness and efficiency of the scheme through experiments in virtual and physical experiment environments as well as comparing and analyzing CCAP with state-of-the-art work.

Vehicle Ad-hoc Network (VANET) faces a large number of potential threats due to its openness and complexity. Identity authentication is the basis for resisting various attacks. Common approaches usually employ public key infrastructure, identity-based signature and cryptography-based algorithms, which either bring high computation and storage costs, have certificate issuance, revocation, and management problems, or exist centralization problems. In this paper, we propose an identity authentication scheme for VANET based on consortium blockchain, in which vehicle or Road-Side Unit (RSU) authenticity is verified by on-chain transactions instead of certificates in other blockchain schemes. To the end, a new data structure based on unspent transaction output is introduced to initiate a set of online operations. Furthermore, we put forward an RSU-aided scheme, in which only one additional step, namely fillToken operation, is required to reduce the communication delay of authentication after a vehicle first joins an RSU group through a series of online operations. We implement the proposed scheme in the Hyperledger Fabric platform and conduct security and performance analysis, which shows the effectiveness of our scheme.

Summary Energy efficiency in heterogeneous computing systems has attracted increasing interests due to its economic and environmental impacts during recent decades. Based on power‐aware hardware techniques, such as dynamic voltage frequency scaling, efforts have been made through task scheduling to reduce the total energy consumption for executing a parallel application while maintaining its time efficiency. In this case, energy conscious task scheduling refers to a bi‐objective optimization that aims to minimize the overall completion time (makespan) and the total energy consumption, simultaneously. Existing energy conscious scheduling algorithms conduct energy optimization by means of slack reclamation or a trade‐off function. However, the performance of slack reclamation has been proved to be upper‐bounded and methods relying on trade‐off functions cannot guarantee bi‐objective optimization. In this article, an energy conscious task scheduling algorithm is proposed to tackle the above issues based on the framework of variable neighborhood search. Two neighborhood structures are designed to reduce makespan and the total energy consumption, respectively. Furthermore, a pruning technique is incorporated into the algorithm to accelerate the searching process. Extensive experimental results on both randomly generated and real‐world applications demonstrate that the proposed algorithm improves the time‐efficient schedules on average by 22.4% for the energy consumption and 1.2% for the makespan.

The secure exchange of messages between different communication devices is a major issue of Internet-of-Things (IoT) applications in future smart cities. Current security mechanisms focus on multiple antennas technology, such as spatial modulation (SM), but in space shift keying (SSK), there is still a space to explore. In this article, we propose a secrecy-enhancing SSK scheme for IoT applications by applying security technologies in the physical layer wherein the number of transmit antennas is arbitrary rather than the value of power of two. In this scheme, the security performance of the communication system is improved by using two technologies, namely, artificial noise (AN) and antenna selection. We assume that the application scenario of the SSK system is under the classic eavesdropping model. First, we design ANs according to the channel state information (CSI) to interrupt the eavesdropper and benefit the legitimate receiver via the appropriate cancellation technology. Second, the antenna selection method is designed based on the signal to leakage noise ratio (SLNR) to further boost the secrecy performance by expanding the mutual information difference between the main channel and the eavesdropping channel. Results from our simulations indicate that by the use of the proposed scheme, significant secrecy enhancing can be achieved in terms of bit error ratio (BER) and secrecy rate (SR) when compared with existing schemes. This achieved secrecy enhancing can benefit the suitable IoT communication applications in the smart city environment to avoid the leakage of data transmission.

Duty-cycling techniques have been widely adopted to save energy for energy-constrained wireless sensor networks, while they also cause the sleep latency issue, especially in a multihop linear sensor network (LSN). So the duty-cycling and pipelined-forwarding (DCPF) techniques have been proposed to alleviate this issue. However, most of existing DCPF protocols have no effective scheme to handle the contention and interference among those proximately-located nodes which maintain the same sleep-wakeup schedule. As a result, the network performance degrades with low energy efficiency and high packet delivery latency, particularly when experiencing a heavy traffic load. To this end, this paper proposes an enhanced pipelined data collection (EPDC) MAC protocol for LSN. In EPDC, three algorithms are proposed to guarantee that those nodes located within the interference range of each other have mutually staggered sleep-wakeup schedules, so that the contention and interference among them can be eliminated. The extensive OP-NET simulations show that EPDC significantly outperforms an existing DCPF protocol in terms of packet delivery ratio, network throughput, packet delivery latency, and energy efficiency.

The authentication protocol is commonly served as the first defense line against various attacks in vehicular ad hoc networks (VANETs). Conventional schemes usually employ public key infrastructure or cryptography-based algorithms, which suffer from high computational and storage cost. In this paper, we propose a privacy-preserving authentication scheme for VANETs based on consortium blockchain. The authenticity of a vehicle or a road-side unit is represented by its transaction capability on blockchain instead of a certificate or a cryptographic key. In support of that, we design a novel data structure based on the unspent transaction output (UTXO) combined with a set of online operations, including issue, transfer, query and revocation. Thus, the authentication between two entities is accomplished by on-chain verification and corresponding communications. We conduct a set of security and privacy analysis as well as implementing a prototype on the Hyperledger Fabric platform, to evaluate the effectiveness and the efficiency of the proposed scheme.

The environment of wireless sensor networks (WSNs) makes the communication not only have the broadcast nature of wireless transmission, but also be limited to the low power and communication capability of sensor equipment. Both of them make it hard to ensure the confidentiality of communication. In this paper, we propose a cluster-based cooperative jamming scheme based on physical layer security for WSNs. The mathematical principle of the scheme is based on the design principle of code division multiple access. By using the orthogonality of orthogonal vectors, the legitimate receiver can effectively eliminate the noise, which is generated by the cooperative jamming nodes to disturb the eavesdropper. This scheme enables the legitimate receiver to ensure a strong communication confidentiality even if there is no location or channel advantage comparing with eavesdroppers. Through extensive simulations, the security performance of the proposed scheme is investigated in terms of secrecy rate.

Existing distributed localization methods rarely consider the location privacy preservation problem, which however is nonnegligible. Regarding location privacy, typical solutions rely on a curious-but-honest model, requesting that all participants follow the rule. Different from the existing studies, both honest and dishonest models are considered in this article. We first propose a privacy-preserving distributed localization algorithm (PP-DILOC) by adopting a noise-adding mechanism under the curious-but-honest model. The performance of localization and privacy preservation of PP-DILOC are both theoretically analyzed. Then, in the presence of dishonest nodes, we propose a resilient PP-DILOC (RPP-DILOC), where a time-varying relax factor and an adversary detection procedure are added into PP-DILOC. Theoretical results provide sufficient conditions for the convergence of RPP-DILOC. The privacy levels and the localization performance in the absence/presence of dishonest nodes are evaluated through numerical and experimental results.

With the rapid growth in multimedia information, problems on how to discover the individual interests of users and recommend them with the proper goods have become increasingly difficult. Traditional recommendation algorithms simply utilize user rating logs for recommendations, but ignore lots of useful information which can be expressed as a Heterogeneous Information Network. In this paper, we propose a similarity measure, PW- PathSim, to calculate the relevance between two entities of the semi-symmetric weighted meta paths. Then a similarity regularization based recommendation algorithm is proposed to integrate the similarity of users and items with matrix factorization for recommendations. Furthermore, we compare the PWMFP algorithm with several benchmarks including FunkSVD, HeteFM and DSR. Experimental results with Douban dataset show that it outperforms other HIN-based algorithms in terms of recommendation accuracy.

Unlike traditional multilateration techniques which usually require multiple anchor nodes to participate in localization process, this paper proposes a novel Single Anchor Localization (SAL) scheme employing target movement. The scheme requires the anchor node to only measure its distance to the target node and the target node to measure its moving distance along straight lines. Two algorithms, i.e., SAL based on two rectilinear movings and a triangular moving, are proposed depending on whether the target node also needs to measure its turning angle or not. The moving distance of the target can be estimated using human step or height, geographical indication, vehicle wheel, etc. Some targets may be able to exactly measure its moving distance, e.g., a vehicle equipped with speed sensors. The turning angle of the target can be obtained by using an angle measurement or compass application installed in a mobile phone or a steering wheel angle sensor installed in a vehicle. Especially, a 90° turning angle can be easily achieved through human eye estimation if the terrain allows a right-angle turn. Extensive simulations are conducted to evaluate the performance of the algorithms, investigating the effects of a set of parameters. The proposed SAL scheme not only reduces the complexity and cost of the localization system, but also provides targets with a localization opportunity in a harsh environment where only one anchor can be attached.

Localization has drawn much attention in the Internet of Things (IoT) era. Under traditional multilateration techniques, existing solutions usually need multiple anchor nodes to perform localization, which introduces more system complexity and cost. In this paper, the single anchor localization (SAL) is first modeled, where a multi-antenna anchor node is able to estimate the location of the target node using both angle and distance information. Then, according to SAL, we propose an accurate and distributed localization (ADL) algorithm, which can not only estimate the location of the target node with fewer anchor nodes but also be more accurate than the traditional multilateration method. Furthermore, we prove that the location estimate under ADL can converge towards the real location of the target node with probability 1. The lower and upper bounds of ADL are also derived under a bounded noise model. Extensive simulations are conducted to demonstrate the performance of ADL and the correctness of the theoretical results.

Due to the vast demand for monitoring a structure or area in linear topology, linear sensor networks (LSNs) have recently attracted plenty of attention. Since sensor nodes are usually battery-powered, duty-cycling techniques have been widely studied to improve energy efficiency, which, however, introduces a significant issue known as sleep latency. Thereafter pipelined forwarding has been proposed in the literature as a promising way to alleviate this issue. This paper focuses on interference analysis for data collection services in a multihop LSN running a duty-cycling and pipelined-forwarding protocol, where multiple concurrent transmissions along a data collection path can severely interfere with each other. We first obtain the nodal distance distributions associated with all concurrent transmissions. Based on the obtained distance distributions and the path-loss model in an interference-limited environment, we analyze the distributions of signal-to-interference-plus-noise ratio (SINR) and link capacity. The obtained SINR distribution indicates the link outage probability at a given SINR threshold. By investigating the transmission which receives the strongest cumulative interference, our model can provide useful guidelines for duty cycle setting to achieve a desired network performance.

Due to the advantages including low cost, low power, massive connections, and wide coverage, and with the assistance of fog computing to achieve low latency, location awareness, etc., narrow-band Internet of Things (NB-IoT) can be widely applied in industry. Many NB-IoT industrial applications need a positioning feature for tracking, fault localization and fast repair, etc. When dedicated positioning systems, e.g., Global Position System, are unavailable for a low-power, low-cost NB-IoT device, it is necessary to rely on terrestrial cellular network for localization. Existing performance studies in NB-IoT cellular-network-based positioning have been mostly carried out by targeting deterministic scenarios. However, it is hard to provide general insights with changing system design parameters and propagation effects, due to the random variations in device location, network coverage, channel condition, etc. This paper develops a general analytical model to study the NB-IoT positioning performance. The location randomness of the device to be localized is considered through the distance distributions between the device and its surrounding evolved node Bs (eNBs). This makes it possible for using the powerful probabilistic distance-based tools from geometric probability to derive the probability that at least a fixed number of participating eNBs can be heard by the device. The model considers both the interference and the NB-IoT system-related parameter settings. It also includes modeling the eNB transmission coordination and traffic load. The obtained results reveal how participating eNBs impact the NB-IoT positioning performance, with the effects of a set of parameter settings investigated.

Narrowband Internet of Things (NB-IoT) is one of the most promising technologies for low-power, wide-area, and low-traffic applications. In NB-IoT, random access is implemented in media access control layer to resolve the channel contention conflict among multiple user equipments (TIEs), and is crucial to the throughput performance of NB-IoT. Previous results in long-term evolution cannot be directly applied due to specification differences. In this paper, we take the first attempt to systematically analyze the performance of random access in NB-IoT. First, after extensively studying the backoff mechanism, we characterize the probability that a TIE initiates random access, the probability that a packet is transmitted successfully and the probability that a channel is busy. Then, we define each TIE's buffer as a first-in-first-out queue. We employ Markov chain to model retransmission number caused by collisions and the length of the queue simultaneously. By exploiting the characteristic of the steady-state distribution of the Markov chain, the above three probabilities in steady state can be obtained explicitly. Based on these probabilities, we calculate the system throughput in terms of TIE number, packet generation rate, retransmission number, and the length of the queue. Finally, we investigate the system throughput and conduct extensive simulations under various parameters, which validate our analysis.

To improve energy efficiency for energy-constrained Wireless Sensor Networks (WSNs), duty-cycling techniques have been widely studied and adopted in the design of Media Access Control protocols. On the other hand, to alleviate the well-known sleep latency issue caused by duty-cycling techniques, the study on pipelined forwarding over duty cycling has also attracted plenty of attention from researchers. Noticing that in the current literature for a typical duty-cycled pipelined-forwarding protocol, there is lack of physical interference model which takes into account the effect of cumulative interference, this paper fills the gap by proposing such a model based on nodal distance distributions. Based on the model, the distribution of Signal-to-Interference-plus-Noise Ratio (SINR) achieved at the receiver can be obtained, and the performance metrics that are functions of SINR, such as outage probability and link capacity, can be analyzed. We utilize the proposed model to conduct performance evaluations for a WSN with the pipelined- forwarding feature and investigate the tradeoff among packet delivery latency, energy efficiency, and network capacity by setting an important network parameter, called sleep factor, which determines how long a node can turn its radio off every cycle.

Summary In Vehicular Ad hoc Networks (VANETs), because of the selfishness of the vehicles, the resource allocation in VANET has become one of the primary tasks. Exploiting the Road Side Units (RSUs), which constructs the Cloud Computing environment, provides more data access opportunities and stable communication time for the vehicles. We investigated the cloud resource allocation for data access with noncooperative game based on a Gauss–Seidel iteration method. We further proposed a repeated game scheme, which can approximately achieve the near Pareto‐optimal flow allocation among the vehicles. Considering the vehicles' irrational behavior, a punishment strategy was designed to prevent the vehicles from behavior deviation. The analysis based on these models lays a theoretical method foundation on cloud resource allocation process. The validity of the modeling and the accuracy of the analysis were verified through the extensive simulations, which also guide the future design of more sophisticated cloud resource allocation schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Device-to-device (D2D) communications in cellular networks are promising technologies for improving network performance. However, they may cause severe intra/inter-cell interference that can considerably degrade the performance of cellular users, and vice versa. Therefore, interference analysis has been one of the most important research topics in such a system. Focusing on an uplink resource reusing scenario, this paper presents a framework based on a probabilistic distance and path-loss model to obtain the distributions of signal, interference, and further Signal-to-Interference-plus-Noise Ratio (SINR), based on which, the performance metrics that are functions of SINR can be analyzed, such as outage probability and capacity. Different from the previous work, this proposed framework: 1) obtains interference and SINR distributions for both cellular and D2D communications, through which insights into their performance metrics and mutual influence are provided and 2) has no limitations on cell shapes, except that they are approximated by polygons or circles. The framework can also be applied to a downlink reusing scenario. Our results indicate that the developed framework is helpful for network planners to effectively tune the network parameters, and thus to achieve the optimum system performance for both cellular and D2D communications.

Modern wireless broadband networks are crucial for different mission-critical applications and public safety agencies. Various natural disasters and physical attacks would result in the malfunction or failure of wireless and cellular infrastructures. This subsequently affects the correct functioning of the dependent mission- critical applications. Thus, disaster management and response is of great importance. In this paper, we focus on disaster response using D2D communications to extend the coverage of base stations, and on controller-assisted routing to maximize the total end-to-end throughput for all of the current flows from the area without network coverage using the ant colony optimization. The proposed routing scheme outperforms the schemes based on the shortest path routing in terms of the total throughput as well as fairness in allocating the rates to the flows.

Mobile Elements (MEs) are widely employed for data collection in Wireless Sensor Networks (WSNs) to balance the energy consumption and prolong the network lifetime. However, the limited travel speed of the MEs causes a large data collection latency, which in turn degrades the performance of the data collection task and weakens the applicability of the data collection schemes. Considering the deterministic concentric circle-like trajectories-covered sensing field, we propose a Low-latency Data Sweeping scheme, LDS, which utilizes the communication opportunities among MEs to shorten the data collection latency. The data are swept by the MEs, which traverse along the trajectories, and are relayed to the adjacent MEs towards the sink node. Besides, the transmission range of sensor nodes is limited to the minimum distance to the nearest trajectory for energy-conservation purposes. The performance of the proposed scheme is first analyzed with probabilistic methods. Extensive simulations further show that our scheme outperforms the well-known heuristic algorithms in terms of the data collection latency, energy dissipation, and network lifetime.

Tiered networks have been introduced to mitigate the issues related to poor cellular coverage in dead zones and indoor environments. However, the large-scale deployment of multiple tiers can result in severe intra-tier and inter-tier interference that can considerably degrade the performance of users in all tiers. Thus, the network interference analysis has been an important topic in tiered networks. In this paper, we focus on the uplink resource reusing scenario in a two-tier cellular network consisting of a macro cell and multiple femto cells. Without imposing any limitations on the shape of the macro/femto cells (as long as they are approximated by polygons), for the first time in the literature, we obtain the distance distributions associated with tiered structures. Utilizing these distance distributions and the path-loss model in an interference-limited environment, we obtain the distributions of the received signal and interference for both tiers. Further, we give details on how our approach applies to the downlink resource reusing scenario as well as a network with multiple macro cells. Our performance study provides insights into the Signal-to-Interference Ratio and outage probability for macro/femto-cell base stations.

Device-to-device (D2D) communications in cellular networks are promising technologies for improving network throughput, spectrum efficiency, and transmission delay. In this paper, we first introduce the concept of guard distance to explore a proper system model for enabling multiple concurrent D2D pairs in the same cell. Considering the Signal to Interference Ratio (SIR) requirements for both macro-cell and D2D communications, a geometrical method is proposed to obtain the guard distances from a D2D user equipment (DUE) to the base station (BS), to the transmitting cellular user equipment (CUE), and to other communicating D2D pairs, respectively, when the uplink resource is reused. By utilizing the guard distances, we then derive the bounds of the maximum throughput improvement provided by D2D communications in a cell. Extensive simulations are conducted to demonstrate the impact of different parameters on the optimal maximum throughput. We believe that the obtained results can provide useful guidelines for the deployment of future cellular networks with underlaying D2D communications.

Geometrical distance distribution (GDD) between nodes in wireless communication networks plays a significant role in modeling network performance metrics. Existing work on obtaining GDD assumes the network geometry to be a regular one, such as circle and square. Due to the various complex effects of wireless signals, however, the network geometry usually is quite irregular. Therefore, this paper proposes a novel systematic and unified approach to obtain the GDD between two random nodes associated with arbitrary network geometries. To the best of our knowledge, this is the first work that will fill the gap in the literature of this field.

The connectivity of ad hoc networks has been extensively studied in the literature. Most recently, researchers model ad hoc networks with two-dimensional lattices and apply percolation theory for connectivity study. On the lattice, given a message source and the bond probability to connect any two neighbor vertices, percolation theory tries to determine the critical bond probability above which a giant connected component appears. This paper studies a related but different problem, directed connectivity: what is the exact probability of the connection from the source to any vertex following certain directions? The existing studies in math and physics only provide approximation or numerical results. In this paper, by proposing a recursive decomposition approach, we can obtain a closed-form polynomial expression of the directed connectivity of square lattice networks as a function of the bond probability. Based on the exact expression, we have explored the impacts of the bond probability and lattice size and ratio on the lattice connectivity, and determined the complexity of our algorithm. Further, we have studied a realistic ad hoc network scenario, i.e., an urban VANET, where we show the capability of our approach on both homogeneous and heterogeneous lattices and how related applications can benefit from our results.

This paper presents the design of a duty-cycling MAC, called PRI-MAC (Pipelined-forwarding, Routing-integrated, and effectively-Identifying MAC), for large-scale wireless sensor networks. PRI-MAC divides the whole network into grades around the sink node. The higher grade a node is in, logically the further away it is from the sink which is in the lowest grade. Staggered sleep-wakeup schedules are established between any two adjacent grades such that data can be forwarded in a pipelined fashion, largely reducing the packet delivery latency to meet the real-time transmission requirement. Meanwhile, the routing function is seamlessly integrated into PRI-MAC, which reduces the protocol overhead and increases the network scalability. Furthermore, each node utilizes a randomly-generated integer as its identifier only when it is involved in a data transmission, instead of allocating a unique address for each sensor node. The performance of PRI-MAC is evaluated in comparison with PW-MAC by OPNET, in terms of the packet delivery latency, energy efficiency, and throughput.

Although the conventional duty cycle MAC protocols such as RMAC for wireless sensor networks (WSNs) perform well in aspects of saving energy and reducing end-to-end delivery latency, they are designed independently and require an extra routing protocol in the network layer to provide path information for the MAC layer. We propose a novel cross-layer duty cycle MAC protocol with data forwarding towards pipeline feature (P-MAC) for WSNs. P-MAC divides all sensor nodes in the network into different grades around the sink. Each node identifies its grade according to its logic hop distance to the sink and simultaneously establishes a sleep/wakeup schedule based on its grade. Those nodes in the same grade keep the same schedule, while which is staggered with the schedule of the nodes in the adjacent grade. Then a variation of RTS/CTS handshake mechanism is used to forward data continuously in a pipeline fashion from the higher grade nodes to the lower grade nodes and finally to the sink. No extra routing overhead is needed, and the superiority of duty-cycling is kept. The performance of PMAC and RMAC are evaluated and compared in terms of packet delivery latency and energy efficiency by OPNET simulation.