Internet Of Things Edge Research Articles

Edge-computing-driven Internet of Things: A Survey

The Internet of Things (IoT) is impacting the world’s connectivity landscape. More and more IoT devices are connected, bringing many benefits to our daily lives. However, the influx of IoT devices poses non-trivial challenges for the existing cloud-based computing paradigm. In the cloud-based architecture, a large amount of IoT data is transferred to the cloud for data management, analysis, and decision making. It could not only cause a heavy workload on the cloud but also result in unacceptable network latency, ultimately undermining the benefits of cloud-based computing. To address these challenges, researchers are looking for new computing models for the IoT. Edge computing, a new decentralized computing model, is valued by more and more researchers in academia and industry. The main idea of edge computing is placing data processing in near-edge devices instead of remote cloud servers. It is promising to build more scalable, low-latency IoT systems. Many studies have been proposed on edge computing and IoT, but a comprehensive survey of this crossover area is still lacking. In this survey, we first introduce the impact of edge computing on the development of IoT and point out why edge computing is more suitable for IoT than other computing paradigms. Then, we analyze the necessity of systematical investigation on the edge-computing-driven IoT (ECDriven-IoT) and summarize new challenges occurring in ECDriven-IoT. We categorize recent advances from bottom to top, covering six aspects of ECDriven-IoT. Finally, we conclude lessons learned and propose some challenging

DeepEdgeSoC: End-to-end deep learning framework for edge IoT devices

The acceleration of deep neural networks (DNNs) on edge devices is gaining significant importance in various application domains. General purpose graphics processing units (GPGPUs) are typically used to explore, train and evaluate DNNs because they offer higher processing and computational capability compared to CPUs. However, this comes at the cost of increased power consumption required by these devices for operation, which prevents efficient deployment of networks on edge devices. In the Internet of Things (IoT) domain, Field programmable gate arrays (FPGAs) are considered a powerful alternative since their flexible architecture can run the DNNs with much less energy. The enormous amount of effort and time required for the entire end-to-end edge-aware deployment urged us to develop DeepEdgeSoc, an integrated framework for deep learning (DL) design and acceleration. DeepEdgeSoc is an overarching framework under which DNNs can be built. DeepGUI, a visual drag-and-drop DNN design environment, plays an important role in accelerating the network design phase. In DeepEdgeSoc, the networks can be quantized and compressed to suite the underlying edge devices in terms of size and energy. DeepEdgeSoc goes beyond the software level by converting the networks to appropriate FPGA implementations that can be directly synthesized and integrated within a System-on-Chip (SoC).

Application and Research of IoT Architecture for End-Net-Cloud Edge Computing

At the edge of the network close to the source of the data, edge computing deploys computing, storage and other capabilities to provide intelligent services in close proximity and offers low bandwidth consumption, low latency and high security. It satisfies the requirements of transmission bandwidth, real-time and security for Internet of Things (IoT) application scenarios. Based on the IoT architecture, an IoT edge computing (EC-IoT) reference architecture is proposed, which contained three layers: The end edge, the network edge and the cloud edge. Furthermore, the key technologies of the application of artificial intelligence (AI) technology in the EC-IoT reference architecture is analyzed. Platforms for different EC-IoT reference architecture edge locations are classified by comparing IoT edge computing platforms. On the basis of EC-IoT reference architecture, an industrial Internet of Things (IIoT) edge computing solution, an Internet of Vehicles (IoV) edge computing architecture and a reference architecture of the IoT edge gateway-based smart home are proposed. Finally, the trends and challenges of EC-IoT are examined, and the EC-IoT architecture will have very promising applications.

Economical Behavior Modeling and Analyses for Data Collection in Edge Internet of Things Networks

Internet of Things (IoT) is progressively becoming an essential aspect of daily life that can be sensed anywhere and anytime, transforming the traditional lifestyle into a high-tech one. Numerous applications in the edge are brought to life based on IoT infrastructures. Especially, edge computing has witnessed the proliferation and impact of IoT-enabled devices benefiting from the data collection and computation capabilities of IoT. However, establishing an IoT from scratch can be monetarily expensive, and leasing the existing sub-networks confronts the potentially dishonest behavior of service providers. To address these issues, we propose a novel framework of leasing edge IoT networks and analyze the influence of sub-network owners’ dishonest behavior on the network. We model the interaction between the edge user and the owners of sub-networks by a Stackelberg game with a unique equilibrium, jointly analyzing the pricing and data collection mechanisms. The Primal-dual Decomposition algorithm and its theoretical analyses are provided for the corresponding strategies of the edge user and sub-network owners. Evaluations demonstrate that the proposed algorithm in the leasing model can save data collection cost up to 53% compared with existing data collection strategies, and illustrate the difference in network performance compared with the game without dishonest owners.

RIHANN: Remote IoT Hardware Authentication With Intrinsic Identifiers

The heterogeneous array of edge devices in an Internet of Things (IoT) infrastructure is increasingly vulnerable to physical in-field tampering attacks. These devices can significantly benefit from a difficult-to-clone and tamper-immune intrinsic identifier that can verify the authenticity or integrity of the physical components. In this article, we develop an intrinsic device identifier, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RIHANN</monospace> , that captures the state of the electronic hardware in an IoT device. This state can adequately reflect any physical tampering of the hardware components by transforming the intrinsic delay variations in the electronic components of an edge device into unique and robust signatures. Our proposed authentication approach utilizes the boundary scan architecture (BSA) in printed circuit boards (PCBs). BSA is a prevalent design for test (DFT) structure used in most PCBs in IoT edge devices. This technique supports an extensive array of heterogeneous devices and can seamlessly operate during the device’s runtime. We measure the boundary scan path delays using the parallel scan delay-measurement (PSDM) technique for commercially available ICs. We perform practical experiments on 20 devices, generate signatures, and evaluate their uniqueness, robustness, randomness, and resistance to aging. We also introduce a security protocol for the cloud server, owner/verifier, or other IoT devices connected to a network to verify their identity remotely. The policy prevents attacks from extracting the device’s secret keys using an efficient moving target defense mechanism that periodically updates and evolves the challenge–response database.

HPoC: A Lightweight Blockchain Consensus Design for the IoT

The research topics of this paper are the data security of the edge devices and terminals of the Internet of Things (IoT) and the consensus design of a lightweight blockchain for the Internet of Things. These devices have self-organization capabilities to overcome the bandwidth delay and service-congestion problems caused by excessive concentration in existing scenarios, but they face the challenges of limited computing, storage, and communication resources. As a result, a non- financial lightweight blockchain consensus design with low energy consumption, low latency, and greater stability should be investigated. We propose a hierarchical proof-of-capability (HPoC) consensus mechanism combined with the asynchronous proof-of-work (PoW) mechanism for improving the computing capacity, storage capacity, and communication capacity of IoT edge devices that can generate blocks with low latency, low power consumption, and strong stability in resource-constrained edge device nodes, while ensuring that the security of the edge devices is enhanced asynchronously. We simulated a smart-home scenario, with the number of device nodes ranging from 15 to 75, and conducted comparative experiments between HPoC and PoW based on different difficulty bits. The experimental results showed that HPoC is a consensus mechanism with scalability and stability that can flexibly adjust time consumption and accurately select nodes with strong capabilities to generate blocks in heterogeneous devices.

EdgeMesh: A hybrid distributed training mechanism for heterogeneous edge devices

AbstractThe proliferation of large‐scale distributed Internet of Things (IoT) applications has resulted in a surge in demand for network models such as deep neural networks (DNNs) to be trained and inferred at the edge. Due to the central data transmission mechanism, heterogeneity of edge devices, and resource constraints, the existing single data‐parallel, and model‐parallel distributed training mechanisms frequently fail to fully utilize the computing power of edge devices, network topology and bandwidth resources. In light of the shortcomings mentioned earlier, this article proposes EdgeMesh, a hybrid parallel training mode based on the Mesh‐Tensorflow framework, consisting of an adaptive meshing strategy and a dynamic model convolutional partitioning strategy. The computing power of IoT edge devices significantly speeds up the DNN training process. In a resource‐constrained environment, each node only supports a subset of the model's parallel computing tasks, reducing communication and memory overhead while retaining high scalability. Experiments show that when compared to single‐machine training and data parallel mode, EdgeMesh distributed training mechanism can reduce the average delay by 3.2 times and average memory overhead by 43% while maintaining model accuracy. The computing power of IoT edge devices effectively accelerates the DNN training process.

Sec-edge: Trusted blockchain system for enabling the identification and authentication of edge based 5G networks

The 5th generation 5G technology, also known as the 2020 telecommunications system, represents the next exciting phase of the evolution of telecommunications, with the recent successful distribution on nearly every continent. 5G is believed to bring multiple advancements such as greater transmission speeds, ultra-low latency, higher network capacity, etc. These advancements will directly support the industry-5.0 and Internet of Things (IoT) in the coming future. Today, IoT is one of the fastest-growing technologies because of its ability to deliver multiple applications across various devices. The emergence of 5G networks will be a major force for IoT development. Subsequently, Edge computing as an extension of cloud computing has emerged as a promising technology to empower 5G and IoT services due to its close vicinity to IoT Devices. However, specific challenging issues plague the integration of 5G, IoT, and Edge Computing, among which security is one of the biggest concerns. Recently, blockchain has been provided as a foundational and decentralized distributed ledger technology that can be used for solving the challenges of IoT, 5G, and Edge Computing integration. This paper proposes a trusted blockchain system for edge-based 5G networks that eliminates the limitations of integrating the Edge Computing, IoT, and 5G networks. The proposed blockchain system can efficiently identify the edge devices, authenticate the devices, and assign the addresses to the edge devices on a demand basis. Further, the proposed work also provides the secure communication among the edge devices that can protect from DDOS and Side-channel attacks.

A Novel Deep-Learning-Based Robust Data Transmission Period Control Framework in IoT Edge Computing System

This article proposes a novel deep learning-based robust Internet of Things (IoT) sensor data transmission period control (DL-RDTPC) framework in an IoT edge computing system. In general, as the data transmission period of IoT sensors increases, the energy consumption of IoT sensors is reduced, and contrarily, the amount of un-transmitted data (i.e., missing values) becomes continuously accumulated. Therefore, the IoT server is in charge of accurately imputing these missing data for reliable data analysis. By addressing this issue, we newly design the imputation accuracy prediction (IAP) module, which captures the complicated relationships between the imputation accuracy and the data transmission period, in order to estimate the imputation accuracy, precisely. For constructing the IAP, three submodules, which include a stacked bidirectional long short-term memory (Bi-LSTM) model, a multihead convolutional neural network (CNN), and a neural network-based period information encoding network (PIEN) are leveraged. To balance the tradeoff between the imputation accuracy and energy consumption regarding the data transmission period, the multiobjective optimization problem is formulated for minimizing the maximum value of both: 1) the energy consumption of IoT sensors obtained from the analytical model and 2) the imputation accuracy predicted from IAP module. The optimal solution is consequently obtained by utilizing the bisection search algorithm. Extensive performance evaluations validate the effectiveness of the proposed RDTPC algorithm in terms of both the average energy consumption (maximum 68% reduction) and missing data imputation accuracy (maximum 64% RMSE reduction) over other benchmarks. Finally, this article provides a practical implementation of the proposed RDTPC framework via the HTTP protocol under the IEEE 802.11-based WLAN network, as well as interworking with the commercial cloud server.

Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge IoT

Edge computing provides a promising paradigm to support the implementation of Internet of Things (IoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasing network size makes it impractical for centralized data processing due to limited bandwidth, and consequently a decentralized learning scheme is preferable. Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. For RL in a decentralized setup, edge nodes (agents) connected through a communication network aim to work collaboratively to find a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability, and adaptation in complex environments with heterogeneous agents may significantly limit the performance of decentralized RL. Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation and has shown faster convergence than gradient descent-based methods. Therefore, we propose an adaptive stochastic incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL with edge-computing-empowered IoT networks. We provide convergence properties for the proposed algorithms by designing a Lyapunov function and prove that the asI-ADMM has <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(1/k) + \mathcal {O}(1/M)$ </tex-math></inline-formula> convergence rate, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> are the number of iterations and batch samples, respectively. Then, we test our algorithm with two supervised learning problems. For performance evaluation, we simulate two applications in decentralized RL settings with homogeneous and heterogeneous agents. The experimental results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability and can well adapt to complex IoT environments.

Sparsity-Aware Intelligent Spatiotemporal Data Sensing for Energy Harvesting IoT System

In this era of the Internet of Things (IoT), the increasing number of IoT devices benefit from energy harvesting (EH) technology which enables a sustainable data acquisition process, including data sensing, communication, and storing for promoting the well beings of the society. However, intermittent and low EH power confines the data acquisition process. Specifically, due to frequent harvesting power outages and depletion of energy for expensive data transmission to the IoT edge server, insufficient energy is allocated for data sensing resulting in the missing of key information. To address this issue, this article proposes a sparsity-aware spatiotemporal data sensing framework for EH IoT devices to minimize the data sensing rate/energy while acquiring comprehensive information and reserving sufficient energy. In this framework, the IoT devices sample critical sparse spatiotemporal data, and then the sparse data are sent to the edge server for reconstruction. To maximize the reconstruction accuracy subject to the limited power supply and intermittent work patterns of EH devices, we first propose the QR-based algorithm QR-ST to initiate a sensing scheduling for each EH device. Due to the unstable and intermittent work pattern, the schedule needs to be dynamically fine-tuned based on environmental inputs. Therefore, we further propose a multiagent deep reinforcement learning-based method named S-Agents for the IoT edge server to globally select the sensing devices at each time slot, where the spatial and temporal features of reconstructed data are guaranteed. Experimental results show that the proposed framework reduced the reconstruction error by 66.30% compared with baselines.

EIoT-PBFT: A multi-stage consensus algorithm for IoT edge computing based on PBFT

With the introduction of edge computing into the field of Internet of Things (IoT), the Cognitive Internet of Things (CIoT) has emerged as the next-generation solution for trust and intelligent reasoning in the IoT. That also puts blockchain, with its unique consensus mechanism, transparency and trustworthiness, on the stage of IoT applications. At present, not much research is focused on blockchain's application in CIoT, whose development is to a large extent restricted by the inefficiency of the consensus algorithm. Considering the characteristics of CIoT, a multi-stage consensus algorithm of EIoT-PBFT is proposed on the basis of PBFT algorithm, which includes the Grouping stage, Scoring stage and Consensus reaching stage. EIoT-PBFT meets the IoT edge computing setup by adopting a two-phase improved PBFT algorithm and a scoring mechanism based on both location and reputation, thus achieving a great increase in consensus efficiency. Evaluation results show that EIoT-PBFT takes 36.4% less time than PBFT for a single consensus, and the performance remains stable over the 2500 node configurations we set up. Moreover, at a scale of 1000 nodes, the number of edge nodes to be configured to reduce the number of system communications by 90% compared to the PBFT algorithm is only 5, making blockchain more customized for CIoT settings.

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)–edge–fog–cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for “pulverization”: applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT–edge–fog–cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings.

Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems

Internet‐of‐things (IoT) edge devices with a memristive neuromorphic system can more effectively enhance daily lives. However, cyberattacks remain critical concerns for smart IoT edge devices that process a vast body of information via networks. Herein, a highly secure neuromorphic system is reported, which can be implemented using a physically unclonable function (PUF) that exploits the high entropy achieved via the stochastic switching of a poly(1,3,5‐trivinyl‐1,3,5‐trimethyl cyclotrisiloxane) (pV3D3)‐based memristor. The excellent insulating property of pV3D3 enhances the stochasticity of the tunneling distance for randomly ruptured Cu filaments. The pV3D3 memristor‐based PUF (pV3D3‐PUF) achieves near‐ideal 50% averages for uniformity and uniqueness, excellent reliability under conditions of mechanical stress and water immersion, and reconfigurability‐bolstering security without additional hardware. Using stochastic in‐memory computing, the pV3D3‐PUF shows resilience to machine learning attacks. Furthermore, a cryptography protocol is demonstrated, which enables artificial intelligence service implementation without security issues for PUF‐integrated pV3D3 memristor‐based neuromorphic systems.

Editorial for the Special Section on Energy-Efficient Edge Computing

The papers in this special section focus on energy efficient edge computing. The future increase in the amount of data and workloads generated by Internet of Things (IoT) devices and connected sensors will lead to the necessity to move computational nodes from the cloud data centers closer to the data source, i.e., at the edge of the cloud, for reduced latency. An edge system is composed of any computing and networking resources along the path between data sources and cloud data centers. Depending on the specific computing needs, edge computing devices can use either a wireless or a wired connection to exchange messages with the data sources. IoT devices and sensors can then exploit the hierarchical structure of the edge and cloud system to analyze the collected data and provide useful information to users in a timely manner. For example, wearable sensors could use the computing resources of the user’s smartphone, laptop, or even smart vehicle to analyze the collected data. Because a large majority of edge devices are battery operated and have limited connectivity, the energy efficiency of computation becomes critical. To this end, it is important to minimize the energy consumption of all the components of an edge system, including sensors, IoT devices, edge nodes, and network devices while guaranteeing the desired performance. For this special section we selected eight articles that cover experimental, conceptual, and theoretical contributions to energy-efficient edge computing.

Analysis of the NTRU Post-Quantum Cryptographic Scheme in Constrained IoT Edge Devices

Research on post-quantum cryptography aims to solve the problematic of modern public-key cryptography being broken by attacks coming from quantum computers in the future and, moreover, by using classical electronics. This task is so critical that the National Institute of Standards and Technology (NIST) is in the final process of standardizing post-quantum schemes for the future protection of embedded applications. Though there are some research work done on embedded systems, it is important to study the impact of these proposals in realistic environments for the Internet of Things (IoT), where the limited computational resources and the strict requirements for power consumption can become incompatible with the usage of cryptographic schemes. In this work, the performance of one of the finalists of the standardization process called NTRU is studied and implemented in a custom wireless sensor node designed for applications in the extreme edge of the IoT. The cryptosystem is implemented and evaluated within the processes of the Contiki-NG operating system. Furthermore, additional experiments are performed to check if commonly integrated hardware peripherals for cryptography inside modern microcontrollers can be used to achieve better performance with NTRU, not only at the single node level but also at the network level, where the NTRU key encapsulation mechanism is tested in a real communication process. The results derived from these experiments show that NTRU is suitable for modern microcontrollers targeting wireless sensor networks design, while old devices present in popular platforms might not afford the cost of its implementation.

Research on energy saving of computer rooms in Chinese colleges and universities based on IoT and edge computing technology

To solve the problems of low overall service quality of the university computer room, unstable environment control of the computer room, low adaptive adjustment ability, and high energy consumption. This article takes Chinese universities as an example to analyze university computer room supervision status, use the Internet of Things (IoT) to remotely and automatically monitor the computer room environment and energy consumption, and analyze the amount of data generated by the rapid increase of IoT edge devices. The method and model of edge computing in the computer room energy consumption monitoring system are proposed through research. The monitoring methods of critical parameters such as the computer room's thermal environment and energy consumption are given. Corresponding solutions for computer room management, testing, use, and energy-saving services are given. It provides a brand-new idea for energy saving in colleges and universities and network room security.

Digital Keying Enabled by Reconfigurable 2D Modulators.

Energy, area, and bandwidth efficient communication primitives are essential to sustain the rapid increase in connectivity among internet-of-things (IoT) edge devices. While IoT edge-sensing, edge-computing, and edge-storage have witnessed innovation in materials and devices, IoT edge communication is yet to experience such transformation. The aging silicon (Si)-based complementary metal-oxide-semiconductor (CMOS) technology continues to remain the mainstay of communication devices where they are used to implement amplitude, frequency, and phase shift keying (amplitude-shift keying [ASK]/frequency-shift keying [FSK]/phase-shift keying [PSK]). Keying allows digital information to be communicated over a radio channel. While CMOS-based keying devices have evolved over the years, their hardware footprint and energy consumption are major concerns for resource constrained IoT communication. Furthermore, separate circuit designs and hardware elements are needed for each keying scheme and achieving multibit modulation to improve bandwidth efficiency remains a challenge. Here, a reconfigurable modulator is introduced that exploits unique ambipolar transport and programmable Dirac voltage in ultrathin MoTe2 field-effect transistors to achieve ASK, FSK, and PSK modulation. Furthermore, by integrating two programmed MoTe2 field-effect transistors, multibit data modulation is demonstrated, which improves the bandwidth efficiency by 200%. Finally, a frequency quadrupler is also realized exploiting the unique "double-well" transfer characteristic.

ARES: Adaptive Resource-Aware Split Learning for Internet of Things

Distributed training of Machine Learning models in edge Internet of Things (IoT) environments is challenging because of three main points. First, resource-constrained devices have large training times and limited energy budget. Second, resource heterogeneity of IoT devices slows down the training of the global model due to the presence of slower devices (stragglers). Finally, varying operational conditions, such as network bandwidth, and computing resources, significantly affect training time and energy consumption. Recent studies have proposed Split Learning (SL) for distributed model training with limited resources but its efficient implementation on the resource-constrained and decentralized heterogeneous IoT devices remains minimally explored. We propose Adaptive REsource-aware Split-learning (ARES), a scheme for efficient model training in IoT systems. ARES accelerates training in resource-constrained devices and minimizes the effect of stragglers on the training through device-targeted split points while accounting for time-varying network throughput and computing resources. ARES takes into account application constraints to mitigate training optimization tradeoffs in terms of energy consumption and training time. We evaluate ARES prototype on a real testbed comprising heterogeneous IoT devices running a widely-adopted deep neural network and dataset. Results show that ARES accelerates model training on IoT devices by up to 48% and minimizes the energy consumption by up to 61.4% compared to Federated Learning (FL) and classic SL, without sacrificing model convergence and accuracy.

LAAP: Lightweight Anonymous Authentication Protocol for IoT Edge Devices Based on Elliptic Curve

The massive heterogeneous devices and open channels of the Internet of Things (IoT) lead to low efficiency and privacy leakage in the authentication process, which brings great challenges to identity authentication. This paper focuses on the anonymous authentication between the IoT edge device and the cloud server. In this work, we first propose a novel lightweight anonymous authentication protocol (LAAP) to meet security and efficiency requirements. Especially, the proposed protocol uses dynamic pseudonyms to prevent the traceable attacks caused by fixed identity identification and also uses symmetric encryption to optimize the server’s search for anonymous device information, and the time complexity is reduced from O n to O 1 . Then, the formal security analysis and informal security analysis are provided to prove the security of the proposed protocol. Finally, extensive numerical results indicate that the proposed LAAP protocol is superior to the benchmarks in terms of computing overhead and communication overhead, while the storage overhead is consistent with the lowest level among other protocols.