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As the Industrial Internet of Things (IIoT) continues to advance, ensuring robust data privacy in data aggregation processes becomes paramount. This paper introduces a novel approach, the federated recurrent-based adaptive battle royale (FR-ABR) algorithm, designed to address challenges such as latency, scalability, centralized aggregation, and data exposure in privacy-preserving data aggregation schemes within the IIoT. Leveraging federated learning, the FR-ABR algorithm employs Recurrent Neural Networks (RNNs) trained on decentralized edge devices, allowing each device to process local data without central data exposure. The study further integrates an adaptive strategy with battle royale optimization to fine-tune hyperparameters and enhance the overall performance of the FR-ABR method. Evaluation metrics including network lifetime, end-to-end delay, latency, throughput, energy consumption, communication overhead, and computation cost are employed to evaluate the performance of the FR-ABR method. Comparative analysis against existing methods establishes the superior performance of the FR-ABR algorithm, showcasing its potential as a robust solution for privacy-preserving data aggregation in IIoT applications. Experimental results presented in this study provide valuable insights into the efficacy and efficiency of the FR-ABR method within the realm of signal, image, and video processing for industrial networks. [ABSTRACT FROM AUTHOR]

In recent years, with the development of artificial intelligence, deep learning has become one of the most important computing loads today. The next generation of artificial intelligence(AI)and high-performance computing applications have put unprecedented demands on the computing power and communication capabilities of computing platforms. Wafer-scale chips integrate ultra-high-density transistors and interconnect communication capabilities on the entire wafer, so it is expected to provide revolutionary computing power solutions for future AI and supercomputing platforms. Among them, the huge computing resources and unique new architecture of wafer-scale chips pose unprecedented challenges to task mapping algorithms. Related research has become a major focus of academic research in recent years. This paper focuses on studying the mapping methods of AI tasks on wafer-scale hardware resources. By expressing the AI algorithm as multiple convolutional kernels and considering the computational power characteristics of convolutional kernels, a mapping algorithm for wafer-scale chips is designed based on genetic algorithms. The simulation results under a series of mapping tasks verifies the effectiveness of the mapping algorithm and revealed the impact of parameters such as execution time and adapter cost on the cost function. [ABSTRACT FROM AUTHOR]

The use of the Internet of Things (IoT) is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices. In critical infrastructure domains like oil and gas supply, intelligent transportation, power grids, and autonomous agriculture, it is essential to guarantee the confidentiality, integrity, and authenticity of data collected and exchanged. However, the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques. Consequently, designing a lightweight secure data transmission scheme is becoming essential. In this article, we propose lightweight secure data transmission (LSDT) scheme for IoT environments. LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol. We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs. Security and performance analysis indicates that the LSDT scheme is secure, suitable for IoT applications, and performs better in comparison to other related security schemes. [ABSTRACT FROM AUTHOR]

The rapid proliferation of the internet of things (IoT) technology has significantly transformed urban landscapes, giving rise to smart city frameworks that leverage interconnected devices for enhanced efficiency and functionality. In these environments, vast amounts of data are generated by diverse sensors and devices, necessitating advanced strategies for effective data collection and transmission. This paper introduces a novel approach to address data collection and transmission challenges in IoTenabled smart city frameworks. The proposed design integrates IoT-Cloud for efficient data collection and employs the energy efficient reliable data transmission (EERDT) model, optimizing IoT data transmission. The enhanced dragonfly routing algorithm, incorporating the firefly algorithm, enhances data routing efficiency. Experimental results demonstrate EERDT's superiority over energy-aware iot-routing (EAIR) and locationcentric energy-harvesting aware-routing (LCEHAR), revealing significant improvements in communication overhead, data processing latency, and network lifetime. The EERDT exhibits substantial reductions in communication overhead, enhancing overall network performance. The EERDT model showcases lower data processing latency and energy consumption, highlighting its potential for resource-efficient IoT data transmission. This work contributes an innovative solution for smart city IoT networks, emphasizing performance enhancements and resource efficiency. [ABSTRACT FROM AUTHOR]

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSN), sensor nodes communicate with one another and transfer the sensed data to the sink over a wireless medium, making the network vulnerable to a variety of security flaws, including eavesdropping. To provide a secure communication, encryption techniques based on some keys need to be used, and the participating sensor nodes should agree with it. The sensor nodes are resource constrained in terms of energy, memory, and processing power. As a result, the key agreement technique in WSN is nontrivial. Therefore, many key agreement techniques have been developed, however most of them focus primarily on security, which is not considered a relevant concern in the context of energy efficiency. There is a compromise between energy usage and security, so we've proposed an algorithm to address it. Also, the nodes in WSN can be benefited by using symmetric based pairwise key establishment for efficient communication in the network. The proposed algorithm strikes a crucial balance between secure routing and energy consumption within wireless sensor networks, outperforming well-established routing algorithms by delivering optimal results. In this paper, we have proposed a Secure and Energy Efficient Protocol Based on Pairwise Key (SEE2PK) for WSN. In the proposed scheme, nodes need to store only one row of the secure matrix and establish secure communication after exchanging one column of a public matrix, which is based on the neighbor adjacency matrix of a cluster. This scheme benefits from using a mesh router as a Cluster Head (CH) due to more memory and computation power. The storage and computation costs are decreased by employing the adjacency matrix as a public matrix. The performance of secure routing is examined with the standard techniques, and the results indicate that: 1) nodes can establish pairwise communication with other nodes; 2) performance of the proposed scheme is better for network metrics: network lifetime, security, computation cost, scalability, and energy consumption; 3) communication cost and storage cost of the network are decreased. [ABSTRACT FROM AUTHOR]

The mobility of low Earth orbit (LEO) satellites causes the LEO satellite network to experience topology changes. Topology change includes periodic topology change that occurs naturally and unpredictable topology change that occurs due to instability of the inter-satellite link between satellites. Periodic and unpredictable topology change causes frequent topology change, requiring massive communications throughout the network due to frequent route convergence. LEO satellites have limited onboard power because they operate on batteries. The waste of limited satellite onboard resources shortens the lifespan of the satellite, and achieving stable end-to-end transmission is challenging for the network. In this regard, minimizing communication overhead is a fundamental consideration when designing a routing scheme. In this paper, we propose a distributed detour routing scheme with minimal communication overhead. This routing scheme consists of a rapid detour, selective flooding, and link recovery procedures. When a link failure occurs in the network, a rapid detour can detect link failure using only a precalculated routing table. Subsequently, selective flooding searches for the optimal detour point within the minimum hop region and flood to detour point. After link recovery, a procedure is defined to traverse the pre-detour path and switch it back to the original path. The simulation results show that the proposed routing scheme achieves a reduction of communication overhead by 97.6% compared with the n-hop flooding approach. [ABSTRACT FROM AUTHOR]

Intrusion detection in the Internet of Things is becoming increasingly important as the number of connected devices grows. Machine learning algorithms can be applied to detect anomalies in large data sets, making them useful for identifying potential intrusions. However, traditional centralized learning techniques entail collecting data from end devices in one central device for training. Allowing a single entity to have access to vast amounts of personal data raises many security concerns as any issue experienced with the system can lead to widespread data leakage. To prevent these issues, it is critical to seek more secure alternatives such as federated learning. It enables multiple parties to collaborate on the same model without having to share the data between them. This process not only helps protect data privacy, but also reduces the risk of data leakage and improves training efficiency. In this paper, we propose a federated-based intrusion detection system. To better investigate the performance of the proposed model, we considered client-side evaluation whereby in the same round, the clients transfer the local models to the server which aggregates them in an updated global model. Then, the server transfers the updated global model to the clients for evaluation. The clients evaluate the global model locally and send back the results to the server to be aggregated using metric aggregation function. The experimental results show that the proposed federated-IDS achieves a high detection rate. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Federated learning (FL) can learn a shared global model across multiple client devices without breaching privacy requirements. But an essential challenge is that devices in FL usually have limited network bandwidth, resulting in inefficient communication as an important bottleneck for FL implementation. Current studies try to overcome this shortcoming by compressing the number of model update bits uploaded by every client. But they did not explore the underlying reason why redundant parameters are generated. In this paper, we propose Corset-Based Federated Learning framework (CBFL) —a novel FL communication framework from the perspective of redundancy data. Instead of training full datasets on a regular network model, CBFL trains a much smaller evolution network model on extracted corset , which intrinsically reduces the overall transmission bits and obtains efficient computation while maintaining a desirable model accuracy. In CBFL, a novel Fedcorset construction algorithm at selected clients and a further distributed model evolution scheme to fit the constructed corset are included. The training model size is dynamically adapted to the corset, either removing a fraction of unimportant or adding important connections at each communication iteration. Experimental results show that CBFL transfers about 13% of communication bits and saves around 56% computing time while having only 2% destination in model accuracy. [ABSTRACT FROM AUTHOR]

With the development of the power internet of things, the traditional centralized computing pattern has been difficult to apply to many power business scenarios, including power load forecasting, substation defect detection, and demand-side response. How to perform efficient and reliable machine learning tasks while ensuring that user data privacy is not violated has attracted the attention of the industry. Blockchain-based federated learning (FL), proposed as a new decentralized and distributed learning framework for building privacy-enhanced IoT systems, is receiving more and more attention from scholars. The framework provides some advantages, including decentralization, scalability, and data privacy, but at the same time its consensus mechanism consumes a significant amount of computational resources. Moreover, the number of model parameters has increased dramatically, leading to an increasing amount of transmitted data and a vast communication overhead. To reduce the communication overhead, we propose an FL framework in the directed acyclic graph (DAG)-based blockchain system, which achieves efficient and trusted sharing of FL networks. We design an adaptive model compression method based on k-means to compress the FL model and reduce the communication overhead of each round in FL training. Meanwhile, the original accuracy-based tips selection algorithm is optimized, and a tips selection algorithm considering multi-factor evaluation is proposed. Simulation experimental results show that the method proposed in this paper reduces the total bytes of communication of the blockchain-based federated learning system while balancing the accuracy of the FL model compared to previous work. [ABSTRACT FROM AUTHOR]

Compared with traditional machine learning,federated learning effectively solves the problems of user data privacy and security protection,but a large number of model exchanges between massive nodes and cloud servers will produce high communication costs.Therefore,cloud-edge-side layered federated learning has received more and more attention.In hierarchical federated learning,D2D and opportunity communication can be used for model cooperation training among mobile nodes.Edge server performs local model aggregation,while cloud server performs global model aggregation.In order to improve the convergence rate of the model,the network transmission optimization technique for hierarchical federated learning is studied.This paper introduces the concept and algorithm principle of hierarchical federated learning,summarizes the key challenges that cause network communication overhead,summarizes and analyzes six network transmission optimization methods,such as selecting appropriate nodes,enhancing local computing,reducing the upload number of local model updates,compressing model updates decentralized training and parameter aggregation oriented transimission.Finally,the future research direction is summarized and discussed.

Vehicular Ad-hoc Network (VANET) is a collection of interconnected smart vehicles. Underlying wireless technology enables communication within vehicles as well as between road infrastructure and vehicles. Security plays a vital role during transmission of safety and important messages due to the open-access environment of VANET. Several security challenges need to be addressed in order to enable a real-time use of VANET. The significant goal of the paper is to propose a Secure Anonymous Routing Protocol (SARP) that is the extension of ACO-IBR. The SARP uses anonymous authentication methodology alongwith bilinear paring technique. The protocol verifies authenticity of the vehicles and integrity of the messages that participate in the communication process at the route discovery phase and it is resistant to various attacks. The simulation based QoS performance of the SARP routing protocol is evaluated using collision rate, packet delivery ratio, delay & throughput. SARP observed 0.023, 0.048, 0.028s less delay and 8.63, 11.52, 10.49 less overhead as compared to the ACO-IBR, GPSR and IBR. The performance is evaluated in terms of communication overhead and computational time that enhance security efficiently. It is observed that SARP requires 224.4ms computational time and 568-byte communication overhead for 100 vehicles. [ABSTRACT FROM AUTHOR]

Distributed base station deployment, limited server resources and dynamically changing end users in mobile edge networks make the design of computing offloading schemes extremely challenging. Considering the advantages of deep reinforcement learning (DRL) in dealing with dynamic complex problems, this paper designs an optimal computing offloading and resource allocation strategy. Firstly, the authors consider a multi‐user mobile edge network scenario consisting of Macro‐cell Base Station (MBS), Small‐cell Base Station (SBS) and multiple terminal devices, the communication overhead and calculation overhead generated are formulated and described in detail. Besides, combined with the deterministic delay of tasks, the optimization objective of this paper is clarified to comprehensively consider system energy consumption. Then, a learning algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed to minimize system energy consumption. Finally, simulation experiments show that the authors' proposed DDPG algorithm can effectively optimize the target value, and the total system energy consumption is only 15.6 J, which is better than other compared algorithms. It is also proved that the proposed algorithm has excellent communication resource allocation ability. [ABSTRACT FROM AUTHOR]

The mobility of low Earth orbit (LEO) satellites causes the LEO satellite network to experience topology changes. Topology change includes periodic topology change that occurs naturally and unpredictable topology change that occurs due to instability of the inter-satellite link between satellites. Periodic and unpredictable topology change causes frequent topology change, requiring massive communications throughout the network due to frequent route convergence. LEO satellites have limited onboard power because they operate on batteries. The waste of limited satellite onboard resources shortens the lifespan of the satellite, and achieving stable end-to-end transmission is challenging for the network. In this regard, minimizing communication overhead is a fundamental consideration when designing a routing scheme. In this paper, we propose a distributed detour routing scheme with minimal communication overhead. This routing scheme consists of a rapid detour, selective flooding, and link recovery procedures. When a link failure occurs in the network, a rapid detour can detect link failure using only a precalculated routing table. Subsequently, selective flooding searches for the optimal detour point within the minimum hop region and flood to detour point. After link recovery, a procedure is defined to traverse the pre-detour path and switch it back to the original path. The simulation results show that the proposed routing scheme achieves a reduction of communication overhead by 97.6% compared with the n-hop flooding approach.

Communication overhead, computation load, and convergence speed are three major challenges in the scalability of distributed stochastic optimization algorithms in training large neural networks. In this paper, we propose the approach of hybrid-order distributed stochastic gradient descent (HO-SGD) which strikes a better balance between these three than the previous methods, for a general class of non-convex stochastic optimization problems. In particular, we advocate that by properly interleaving zeroth-order and first-order gradient updates, it is possible to significantly reduce the communication and computation overheads while guaranteeing a fast convergence. The proposed method guarantees the same order of convergence rate as in the fastest distributed methods (i.e., fully synchronous SGD) while having significantly less computational complexity and communication overhead per iteration, and the same order of communication overhead as in the state-of-the-art communication-efficient methods, with order-wisely less computational complexity. Moreover, it order-wisely improves the convergence rate of zeroth-order SGD methods. Finally and remarkably, empirical studies demonstrate that the proposed hybrid-order approach provides significantly higher test accuracies and superior generalization than all the baselines, owing to its novel exploration mechanism. • This paper proposes the novel approach of hybrid-order optimization and learning , which strikes a better balance between communication overhead, computational complexity, and convergence rate for distributed optimization and learning than the previous methods. • The proposed method can solve a general class of non-convex stochastic optimization problems with guaranteed convergence to the stationary point. • The proposed method guarantees the same order of convergence rate (in terms of the numbers of iterations and worker nodes) as in the fastest distributed methods (i.e., fully synchronous SGD), while having significantly less computational complexity and communication overhead per iteration. • The proposed method guarantees the same order of communication overhead as in the state-of-the-art communication-efficient methods, with order-wisely less computational complexity. • The proposed method order-wisely improves the convergence rate of zeroth-order SGD methods. • Finally and remarkably, empirical studies demonstrate that the proposed hybrid-order approach provides significantly higher test accuracies and superior generalization than all the baselines, owing to its novel exploration mechanism. • The paper proposes a novel exploration mechanism resulting in better generalization. [ABSTRACT FROM AUTHOR]

We consider a heterogeneous distributed service system consisting of n servers with unknown and possibly different processing rates. Jobs with unit mean arrive as a renewal process of rate proportional to n and are immediately dispatched to one of several queues associated with the servers. We assume that the dispatching decisions are made by a central dispatcher with the ability to exchange messages with the servers and endowed with a finite memory used to store information from one decision epoch to the next, about the current state of the queues and about the service rates of the servers. We study the fundamental resource requirements (memory bits and message exchange rate) in order for a dispatching policy to be always stable. First, we present a policy that is always stable while using a positive (but arbitrarily small) message rate and log 2 (n) bits of memory. Second, we show that within a certain broad class of policies, a dispatching policy that exchanges o (n 2) messages per unit of time, and with o (log (n)) bits of memory, cannot be always stable. [ABSTRACT FROM AUTHOR]

Tiered Mobile Wireless Sensor Network(TMWSN) is a new paradigm introduced by mobile edge computing. Now it has received wide attention because of its high scalability, robustness, deployment flexibility, and it has a wide range of application scenarios. In TMWSNs, the storage nodes are the key nodes of the network and are more easily captured and utilized by attackers. Once the storage nodes are captured by the attackers, the data stored on them will be exposed. Moreover, the query process and results will not be trusted any more. This paper mainly studies the secure KNN query technology in TMWSNs, and we propose a secure KNN query algorithm named the Basic Algorithm For Secure KNN Query(BAFSKQ) first, which can protect privacy and verify the integrity of query results. However, this algorithm has a large communication overhead in most cases. In order to solve this problem, we propose an improved algorithm named the Secure KNN Query Algorithm Based on MR-Tree(SEKQAM). The MR-Trees are used to find the K-nearest locations and help to generate a verification set to process the verification of query results. It can be proved that our algorithms can effectively guarantee the privacy of the data stored on the storage nodes and the integrity of the query results. Our experimental results also show that after introducing the MR-Trees in KNN queries on TMWSNs, the communication overhead has an effective reduction compared to BAFSKQ.

As a new generation of big data computing engine, Flink has been widely used. When containers of Flink are deployed in cloud environment, its default task scheduling algorithm cannot perceive node resources information and adjust the load in time, and the capacity for independent equilibrium is poorer. Although mainstream container layout tools provide the possibility of container management, they fails to combine Flink characteristics to solve the problem of balancing the resource utilization while reducing the communication overhead in the container group. Aiming at the above problem, this paper proposes a Flink load balancing strategy for cloud environment, which comprehensively considers the distribution characteristics of operators in Flink cluster and the communication mechanism between containers, and takes the communication cost between nodes and load balancing as evaluation criteria. Experimental results show that, compared with Flink default scheduling algorithm, this algorithm can effectively improve the computing efficiency and system performance. [ABSTRACT FROM AUTHOR]

An efficiency-oriented solution is theoretically a preferred choice to support the efficient operation of a system. Although some studies on the multimanipulator system share the load of the control center by transforming the network topology, the whole system often suffers an increased communication burden. In this article, a multimanipulator cooperative control scheme with an improved communication efficiency is proposed to allocate limited communication resources reasonably. The entire control process is formulated from the perspective of the game theory, and finally, evolved into a problem of finding a Nash equilibrium with time-varying parameters. Then, a neural network solver is designed to update the strategies of manipulators. Theoretical analysis supports the convergence and robustness of the solver. In addition, Zeno behavior does not occur under the domination of the control strategy. Finally, simulative results reveal that the proposed control strategy has advantages over the traditional periodic control in communication. [ABSTRACT FROM AUTHOR]

The sixth-generation network (6G) is expected to achieve a fully connected world, which makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging distributed computing paradigm. In Vehicular Edge Computing (VEC), FL is used to protect consumer data privacy. However, using FL in VEC will lead to expensive communication overheads, thereby occupying regular communication resources. In the traditional FL, the massive communication rounds before convergence lead to enormous communication costs. Furthermore, in each communication round, many clients upload large quantity model parameters to the parameter server in the uplink communication phase, which increases communication overheads. Moreover, a few straggler links and clients may prolong training time in each round, which will decrease the efficiency of FL and potentially increase the communication costs. In this work, we propose an efficient-communication approach, which consists of three parts, including “Customized”, “Partial”, and “Flexible”, known as FedCPF. FedCPF provides a customized local training strategy for vehicular clients to achieve convergence quickly through a constraint item within fewer communication rounds. Moreover, considering the uplink congestion, we introduce a partial client participation rule to avoid numerous vehicles uploading their updates simultaneously. Besides, regarding the diverse finishing time points of federated training, we present a flexible aggregation policy for valid updates by constraining the upload time. Experimental results show that FedCPF outperforms the traditional FedAVG algorithm in terms of testing accuracy and communication optimization in various FL settings. Compared with the baseline, FedCPF achieves efficient communication with faster convergence speed and improves test accuracy by 6.31% on average. In addition, the average communication optimization rate is improved by 2.15 times. [ABSTRACT FROM AUTHOR]

Gradient synchronization, a process of communication among machines in large-scale distributed machine learning (DML), plays a crucial role in improving DML performance. Since the scale of distributed clusters is continuously expanding, state-of-the-art DML synchronization algorithms suffer from latency for thousands of GPUs. In this article, we propose 2D-HRA, a two-dimensional hierarchical ring-based all-reduce algorithm in large-scale DML. 2D-HRA combines the ring with more latency-optimal hierarchical methods, and synchronizes parameters on two dimensions to make full use of the bandwidth. Simulation results show that 2D-HRA can efficiently alleviate the high latency and accelerate the synchronization process in large-scale clusters. Compared with traditional algorithms (ring based), 2D-HRA achieves up to 76.9% reduction in gradient synchronization time in clusters of different scale.

In order to improve the utilization of the infrastructure resource and efficiently deploy the service function chain dynamically in network function virtualization,the orchestration management domain needs to monitor the network resources and virtual network function status in real time,but real-time monitoring will bring large communication overhead.An intelligent distributed monitoring strategy with minimal network communication overhead was proposed.The improved label propagation algorithm intelligently subnets and selects agent monitoring nodes to achieve efficient monitoring of resource and virtual function status and minimize monitoring information communication overhead.The simulation results show that the monitoring strategy proposed reduces the monitoring information communication overhead in the network by about 13%.

In order to improve the utilization of the infrastructure resource and efficiently deploy the service function chain dynamically in network function virtualization, the orchestration management domain needs to monitor the network resources and virtual network function status in real time, but real-time monitoring will bring large communication overhead. An intelligent distributed monitoring strategy with minimal network communication overhead was proposed. The improved label propagation algorithm intelligently subnets and selects agent monitoring nodes to achieve efficient monitoring of resource and virtual function status and minimize monitoring information communication overhead. The simulation results show that the monitoring strategy proposed reduces the monitoring information communication overhead in the network by about 13%.

This paper is concerned with the security problem of Time of Arrival (ToA) based localization schemes in a wireless sensor network (WSN) with multiple attackers and this paper focuses on defending against external attacks, especially under cooperative external attackers. The prior scheme for defending against the attacks in the localization scheme often introduce high communication overhead and their security relies on the capability of the attackers. In this paper, we propose a lightweight secure ToA-based localization scheme in a WSN by exploiting the noise feature caused by external distance attacks. In comparison with the prior scheme, our scheme provides lower communication overhead and a higher level of security. We theoretically analyze the performance of the proposed scheme over fading channels and derive the closed-form expressions. We implemented our scheme and conducted extensive performance comparisons through simulations. Our experimental results show that the closed-form expressions for the detection performance perfectly match with their simulation results as we expected. The communication overhead of the proposed scheme is saved by 72.8% than that of the prior scheme for different numbers of anchors and is independent of the times of measurements. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In current days cloud domain gained a tremendous increase of user's attention by several small and large scale companies including Software, BPO, Medical, Schools and a lot more. Since there was very less security in the primitive clouds for storing and accessing the information from the remote locations, still there was a lot of demand for the data which is to be stored in the cloud. As we know that in primitive clouds, there are no concepts like privacy for the data in terms of encryption and message digest in order to provide data authorization. In current days cloud servers are almost dishonest in nature by omitting intentionally some qualified results to save computational resources and communication overhead. In this paper, we try to design a new protocol and analyze the importance of Cuckoo filter over encrypted cloud data to provide data search accurately by the cloud users. Here we try to encrypt the data using AES algorithm and try to generate MAC key for the data which is uploaded and downloaded by the cloud users. For data authorization we use MD5 algorithm and with the help of MD5 algorithm, we can generate a short signature to find out the data verification and finally we try to apply Cuckoo filter for data integrity. This cuckoo filter will try to store the data in duplicated manner and try to provide end user with original data. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) has recently received a lot of attention for large-scale privacy-preserving machine learning. However, high communication overheads due to frequent gradient transmissions decelerate FL. To mitigate the communication overheads, two main techniques have been studied: (i) local update of weights characterizing the trade-off between communication and computation and (ii) gradient compression characterizing the trade-off between communication and precision. To the best of our knowledge, studying and balancing those two trade-offs jointly and dynamically while considering their impacts on convergence has remained unresolved even though it promises significantly faster FL. In this paper, we first formulate our problem to minimize learning error with respect to two variables: local update coefficients and sparsity budgets of gradient compression who characterize trade-offs between communication and computation/precision, respectively. We then derive an upper bound of the learning error in a given wall-clock time considering the interdependency between the two variables. Based on this theoretical analysis, we propose an enhanced FL scheme, namely Fast FL (FFL), that jointly and dynamically adjusts the two variables to minimize the learning error. We demonstrate that FFL consistently achieves higher accuracies faster than similar schemes existing in the literature. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be used in network topology optimization for reducing communication overhead in the Kalman consensus filter. Distributed estimation and tracking of interested objects over wireless sensor networks (WSNs) is a hot research topic. Since network topology possesses distinctive structural parameters and plays an important role for the performance of distributed estimation, we first formulate the communication overhead reduction problem in distributed estimation algorithms as the network topology optimization in this paper. The effect of structural parameters on the algebraic connectivity of a network is overviewed. Moreover, aiming to reduce the communication overhead in Kalman consensus filter (KCF)-based distributed estimation algorithm, we propose a network topology optimization method by properly deleting and adding communication links according to nodes' local structural parameters information, in which the constraint on the communication range of two nodes is incorporated. Simulation results show that the proposed network topology optimization method can effectively improve the convergence rate of KCF algorithm and achieve a good trade-off between the estimate error and communication overhead. [ABSTRACT FROM AUTHOR]

The Yi‐he global spectral model (YHGSM) is a numerical weather prediction (NWP) model developed by the National University of Defense Technology, China. The YHGSM has two optimization techniques that have recently been adopted for the on‐demand communication version of the semi‐Lagrangian scheme: one‐sided communication and an adaptive strategy for the maximum wind speed. However, the limitation of the original model is that the computation and communication cannot be performed simultaneously, resulting in a waste of resources. This article addresses this limitation by dividing the array to be interpolated into three independent subarrays. The communication and interpolation operations for each subarray are rearranged into three groups. This allows the overlap of the communication with computation between different groups. In addition, by adjusting the synchronization operations, the degree of overlap is further increased. In the optimized scheme, the per‐step overhead of the interpolation phase is greatly reduced, and its speedup decreases first and increases afterwards with the increase of the number of cores. Therefore, when the number of cores is large, the benefits from overlapping the communication with computation will be more substantial. [ABSTRACT FROM AUTHOR]

Technological developments in communication technologies in the form of hardware and software have made unilateral sensor connectivity over Internet access that facilitates data observation and measurement of physical entities. A technology known as Internet of Things (IoT) is commonly referred to as the connectivity of Internet devices that provides the communication interactivity between the physical and the cyber objects. One of the key objectives of Internet computing is to simplify human activities and improve the user experience and device access. To explore its basic challenges, big data is somehow diversified into smart-data intelligence that transforms the raw semantic data into smart-data. The transformation approaches realize the significance of productivity and financial gain, which in turn offers a better decision-making process and privacy preservation. Moreover, the intelligent system collects raw data from different devices that analyze the extracted information. Since IoT plays a significant role in the development of a new source dataset, a seamless authentication protocol (SAP) is preferably chosen to coalesce data inference, algorithm development, and technological advancement. The comparative analysis proves that the proposed SAP consumes less computation and communication overhead as compared to other authentication schemes. [ABSTRACT FROM AUTHOR]

Summary: Data Deduplication is the foremost technique used for data compression by removing redundant data. It is broadly utilized for cloud storage server, which reduces the un‐utilized memory space and optimized bandwidth. The confidentiality of sensitive information maintains during Deduplication detection. The Efficient Hash Function–based Duplication Detection (EHFDD) algorithm is proposed to encrypt the content before outsourcing in cloud environment. This paper is an attempt to enhance the data security and notify the issues of approved data Deduplication detections. Furthermore, it also presents several novel Deduplication improvisations for helping certified duplicate verification in hybrid cloud. The EHFDD method enables duplicate detection scheme with minimal overhead compared to existing functions. Based on experimental evaluations, proposed EHFDD algorithm reduces the average delay in 28.7 milliseconds, memory utilization in 8%, computation time in 457 milliseconds, communications overhead in 900 milliseconds, and improves the success rate in 3.13%. [ABSTRACT FROM AUTHOR]

As a new platform in big data stream computing, Apache Heron ignores the difference in communication modes between task instances and the unbalance of processing load among nodes, which leads to the decline system performance. To address the problem, this paper designed the model of node resource limitation, the model of communication overhead optimization and the model of data stream relationships among instances, as the foundation to propose the TUR-Heron. The strategy was composed of the node resource limitation algorithm and the instance reallocation algorithm. By judging the criteria for instance reallocation and executing instance reallocation algorithm, this strategy transformed the inter-node data streams into intra-node data streams and minimized the communication overhead of the system. The experimental results show that under the three sets of benchmarks, TUR-Heron reduces the communication overhead between nodes and the response latency of the system compared with the default scheduling strategy, and improves the balance of resource utilization of computing nodes. [ABSTRACT FROM AUTHOR]

MANET plays a key role in the contemporary improvements in technology and services that dynamically build up the network connection bringing about a variation in network topology. If any malevolent activity inside the network otherwise in the system occurs, it is monitored by software application or device called Intrusion or Invader Detection System (IDS). The power utilization is more in MANET as the IDS needs to be active for the whole time on each node. Considering this, we have designed a clustering-based proposal for Ad-hoc networks. To lower the communication overhead, the CH (Cluster Head) detection is called upon for establishing the path Weighted Clustering Algorithm (DCA) that is utilized to construct path. The proposed Distributed Clustering Algorithm dependent Invader Detection System (DCAIDS) is designed to reduce the overall delay. The intruder attacks are identified by IDS and that particular nodes are detached from the cluster. When the particular target node receives the packets, it acknowledges its reception by sending RREP message so that the sender can send the intended data packets thereafter. Thus, an efficient and delay tolerant path is established which in turn intensifies the certainty of MANET. [ABSTRACT FROM AUTHOR]

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms., Competing Interests: The authors declare that they have no competing interests., (© 2024 Liu et al.)

As Internet-of-Things (IoT) technology emerges, smart manufacturing has recently attracted a large amount of attention. Smart manufacturing leads to smart energy management because of its significant operating expenditure savings. However, it is believed that centralized energy management of IoT devices will impose critically large overhead since massive numbers of IoT devices are expected to be deployed. Therefore, distributed energy management or demand response (DR) is deemed to be a better solution for emerging massive smart manufacturing systems. There have been a significant number of distributed DR algorithms, including Stackelberg game theoretic approaches. However, the Stackelberg game theoretic approaches require a large number of iterations to reach Nash equilibrium, which in turn necessitates communication overheads among IoT devices. This communication overhead causes a large amount of energy consumption as well as delay. In this paper, we propose a light-weight DR scheme based on the Stackelberg model without iterations for the massive smart manufacturing systems. The proposed scheme manages energy consumption based on a non-iterative Stackelberg model and historical real-time pricing. To the best of our knowledge, our approach is the first technique that considers communication overheads for the DR technique. The performance evaluation demonstrates that the proposed scheme shifts operations to avoid peak loads, and the electricity bill is significantly reduced, operations occur at preferred times, and communication energy consumption and delay are minimized.

In large-scale software-defined networking (SDN), the logically centralized controller usually uses multiple distributed controllers to manage and operate the network in a cooperative manner from the perspective of global network view. One of the major challenges with respect to deploying multiple controllers in SDN is how to synchronize the state among controllers to maintain information consistency. Aiming at solving this problem, we propose a periodically adaptive synchronization strategy of controllers. At first, the consistency level of controllers in the network is quantified based on the characteristics of SDN. Then, an adaptive synchronization strategy is proposed. By the use of this strategy, controllers are divided into three kinds of roles, and the synchronization period is adjusted dynamically by an adaptive function according to the current network state to attain a certain consistency level. The simulation results show that the proposed strategy has merits of reducing communication overhead of controllers as well as improving network availability compared with the other non-adaptive strategies. However, it lacks consideration of load balance among controllers, which will be our future research direction.

The idea of aggregate signcryption was first proposed by Selvi. The aggregation process reduces the communication overhead and hence, it is efficient in low‐bandwidth communication networks such as wireless sensor networks and vehicular ad‐hoc network VANET. The goal of this study is to propose a secure provably identity based aggregate signcryption scheme ID‐ASC without pairings over the elliptic curve cryptography. The proposed scheme is provable secure against confidentiality and unforgeability under random oracle model. Moreover, the proposed ID‐ASC reduced the computational complexity when compared to other schemes in literature. [ABSTRACT FROM AUTHOR]

A new dynamic relationship-zone routing protocol (DRZRP) for ad hoc networks is proposed. In this protocol, each node in the network establishes a neighboring-zone with a radius of ρ hops, and activates a relationship-zone according to the service request frequency and service hotspot condition. DRZRP establishes proactive routing for neighboring-zone and relationship-zone of the node, and the relationship-zone of the node can be dynamically maintained, including: initialization, relationship-zone activation, and relationship-zone inactivation. The simulation results are compared with LSR, ZRP and ZHLS protocols, and DRZRP greatly reduces the communication overhead of routing control messages and significantly improves the average delay of routing requests. The new protocol matches the service relationship among nodes in the network, and has comprehensive performance advantage in communication overhead and routing request delay, which improves the quality of network service. [ABSTRACT FROM AUTHOR]