Low Network Overhead Research Articles

Zeroing Unknown Terms: A Novel Clustering Architecture for Low Network Overhead in Distributed Massive MIMO

Zeroing Unknown Terms: A Novel Clustering Architecture for Low Network Overhead in Distributed Massive MIMO

Multi-Copy Relay Node Selection Strategy Based on Reinforcement Learning.

Delay tolerant networks (DTNs), are characterized by their difficulty in establishing end-to-end paths and and large message propagation delays. To control network overhead costs, reduce message delays, and improve delivery rates in DTNs, it is essential to not only delete messages that have reached their destination but also to more precisely determine appropriate relay nodes. Based on the above goals, this paper constructs a multi-copy relay node selection router algorithm based on Q-lambda reinforcement learning with reference to the idea of community division (QLCR). In community division, if a node has the highestdegree, it is considered the core node, and nodes with similar interests and structural properties are divided into a community. Node degree refers to the number of nodes associated with the node, indicating its importance in the network. Structural similarity determines the distance between nodes. The selection of relay nodes considers node degree, interests, and structural similarity. The Q-lambda reinforcement learning algorithm enables each node to learn from the entire network, setting corresponding reward values based on encountered nodes meeting the specified conditions. Through iterative processes, the node with the most cumulative reward value is chosen as the final relay node. Experimental results demonstrate that the proposed algorithm achieves a high delivery rate while maintaining low network overhead and delay.

GMTS: GNN-based multi-scale transformer siamese network for remote sensing building change detection

ABSTRACT With the remarkable success of change detection (CD) in remote sensing images in the context of deep learning, many convolutional neural network (CNN) based methods have been proposed. In the current research, to obtain a better context modeling method for remote sensing images and to capture more spatiotemporal characteristics, several attention-based methods and transformer (TR)-based methods have been proposed. Recent research has also continued to innovate on TR-based methods, and many new methods have been proposed. Most of them require a huge number of calculation to achieve good results. Therefore, using the TR-based mehtod while maintaining the overhead low is a problem to be solved. Here, we propose a GNN-based multi-scale transformer siamese network for remote sensing image change detection (GMTS) that maintains a low network overhead while effectively modeling context in the spatiotemporal domain. We also design a novel hybrid backbone to extract features. Compared with the current CNN backbone, our backbone network has a lower overhead and achieves better results. Further, we use high/low frequency (HiLo) attention to extract more detailed local features and the multi-scale pooling pyramid transformer (MPPT) module to focus on more global features respectively. Finally, we leverage the context modeling capabilities of TR in the spatiotemporal domain to optimize the extracted features. We have a relatively low number of parameters compared to that required by current TR-based methods and achieve a good effect improvement, which provides a good balance between efficiency and performance.

Secure and Efficient Video Transmission in VANET

Currently, vehicular communications have become a reality used by various applications, especially applications that broadcast video in real time. However, the video quality received is penalized by the poor characteristics of the transmission channel (availability, non-stationarity, the ration of signal-to-noise, etc.). To improve and ensure minimum video quality at reception, we propose in this work a mechanism entitled “Secure and Efficient Transmission of Videos in VANET (SETV)”. It's based on the "Quality of Experience (QoE)" and using hierarchical packet management. This last is based on the importance of the images of the stream video. To this end, the use of transmission error correction with uneven error protection has proven to be effective in delivering high quality videos with low network overhead. This is done based on the specific details of video encoding and actual network conditions such as signal to noise ratio, network density, vehicle position and current packet loss rate (PLR) not to mention the prediction of the future DPP.Machine learning models were developed on our work to estimate perceived audio-visual quality. The protocol previously gathers information about its neighbouring vehicles to perform distributed jump reinforcement learning. The simulation results obtained for several types of realistic vehicular scenarios show that our proposed mechanism offers significant improvements in terms of video quality on reception and end-to-end delay compared to conventional schemes. The results prove that the proposed mechanism has showed 11% to 18% improvement in video quality and 9% load gain compared to ShieldHEVC.

MR-Pareto: A Multiattribute Opportunistic Routing Method Based on Pareto Optimal Solution for Mobile Crowdsensing

Mobile crowdsensing (MCS) is a new perception mode for solving large-scale mobile sensing tasks. Traditional data transmission methods are inapplicable, as the MCS is affected by coverage, user preference, and network access cost. Opportunistic network data transmission schemes in MCS have recently witnessed a surge of research efforts due to their ability of high delivery and low consumption. However, existing works only focus on the impact of the geographical location of nodes on user needs or the interaction between social information and data, which do not take into account the temporal and spatial characteristics of nodes. To address these issues, this paper proposes a multiattribute routing method based on Pareto optimal (MR-Pareto) solution to construct a balance between the energy consumption and resource constraints of nodes in transmission protocols. First, the attribute relationship between nodes is analyzed, which was aimed at selecting the nodes within a contact time threshold. Then, based on a nondominated sorting approach, we achieve a Pareto optimal set of candidate nodes. Finally, the relay nodes for forwarding messages are determined by comparing the cache size and the remaining energy. The experimental results demonstrate that our proposed method has low network overhead, low packet loss, and high message delivery rate, compared to epidemic and prophet routing strategies.

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

Local Traffic-aware Green Algorithm based on Sleep-scheduling in autonomous networks

Over recent years, green communication has been investigated to reduce the economic costs and environmental pollution caused by network infrastructures. There exist many approaches to conserve energy in autonomous networks while according to over-provisioning of networking resources for peak traffic periods, sleep-scheduling based approach is one of effective methods between them. In this regards, in this paper, a new Local Traffic-aware Green Algorithm based on Sleep-scheduling (LTGAS) is proposed in autonomous wired networks in order to reduce energy consumption during low traffic periods. This proposal is a dynamic solution that considers online and local traffic knowledge to switch off some underutilized network nodes and links during low traffic periods and re-switch on some sleep devices in congestion situations. In addition, LTGAS is executed in fully distributed mode without any centralized controller and uses only information provided by link-state protocols like OSPF. Moreover, this solution does not have to gather global traffic knowledge of network during a sleep-scheduling decision, resulting in low complexity and low network overhead. The results obtained by the real network scenario show that LTGAS switches off up to 38% of network nodes and up to 96% of the maximum number of switchable links during low traffic periods. In addition, LTGAS has a low effect on network performance metrics, including end-to-end delay, packet delivery ratio and average link utilization, compared with similar proposals while switches off more number of network devices rather than other approaches.

A Routing Query Algorithm Based on Time-Varying Relationship Group in Opportunistic Social Networks

With the fast development of IoT and 5G technologies, opportunity social networks composed of portable mobile devices have become a hot research topic in recent years. However, arbitrary node movement in opportunity networks and the absence of end-to-end pathways make node communication unstable. At the same time, the problem of ignoring human social preferences and relying on wrong message relay nodes lead to a low data transmission rate and high network overhead. Based on the above issues, we propose a time-varying relationship groups-based routing query algorithm for mobile opportunity networks (Time-varying Relationship Groups, TVRGs). Firstly, we construct the relationship groups based on the time-varying characteristics according to the intimacy between users. Secondly, we calculate the importance of nodes by their connectivity time and communication frequency. Finally, we find the suitable message relay nodes according to the similarity of node weights and their action trajectories and design the routing query algorithm accordingly. The simulation results show that the algorithm can vastly improve the message query success rate, effectively improve the data transmission efficiency, and reduce the average delay and system overhead compared with the existing routing algorithms.

Secure and Efficient Video Transmission in VANET

Currently, vehicular communications have become a reality used by various applications, especially applications that broadcast video in real time. However, the video quality received is penalized by the poor characteristics of the transmission channel (availability, non-stationarity, the ration of signal-to-noise, etc.). To improve and ensure minimum video quality at reception, we propose in this work a mechanism entitled “Secure and Efficient Transmission of Videos in VANET (SETV)”. It's based on the Quality of Experience (QoE) and using hierarchical packet management. This last is based on the importance of the images of the stream video. To this end, the use of transmission error correction with uneven error protection has proven to be effective in delivering high quality videos with low network overhead. This is done based on the specific details of video encoding and actual network conditions such as signal to noise ratio, network density, vehicle position and current packet loss rate (PLR) not to mention the prediction of the future DPP. Machine learning models were developed on our work to estimate perceived audio-visual quality. The protocol previously gathers information about its neighbouring vehicles to perform distributed jump reinforcement learning. The simulation results obtained for several types of realistic vehicular scenarios show that our proposed mechanism offers significant improvements in terms of video quality on reception and end-to-end delay compared to conventional schemes. The results prove that the proposed mechanism has showed 11% to 18% improvement in video quality and 9% load gain compared to ShieldHEVC.

A dynamic sleep‐scheduling algorithm based on vertex cover in autonomous systems

SummaryOver recent years, energy conservation in Internet backbone networks has been proposed to reduce economic cost and environmental pollution produced by networking sectors. In this regard, we propose a new dynamic vertex cover‐based green algorithm (DVCGA) in order to conserve energy. Our solution considers online local/global traffic knowledge to switch off some underutilized network devices during nonpeak periods and reswitch on some sleep devices in congestion situation. In addition, DVCGA does not require any centralized controller and uses only information provided by a link‐state protocol. Results, obtained by real network scenario, show that up to 28% of network nodes and up to 82% of the maximum number of switchable links are switched off after DVCGA running. In addition, network performance metrics such as delay and packet delivery ratio during DVCGA running are close to original network and better than similar works. Moreover, the number of device state changes in the effect of DVCGA execution during network running is lower than similar proposals resulting in low network overhead.

Effective communication data transmission based on community clustering in opportunistic social networks in IoT system

With the rise of the Internet of Things, the opportunistic network of portable smart devices has become a new hot spot in academic research in recent years. The mobility of nodes in opportunistic networks makes the communication links between nodes unstable, so data forwarding is an important research content in opportunistic networks. However, the traditional opportunistic network algorithm only considers the transmission of information and does not consider the social relationship between people, resulting in a low transmission rate and high network overhead. Therefore, this paper proposes an efficient data transmission model based on community clustering. According to the user’s social relationship and the release location of the points of interest, the nodes with a high degree of interest relevance are divided into the same community. Weaken the concept of a central point in the community, and users can share information to solve the problem of excessive load on some nodes in the network and sizeable end-to-end delay.

Construction of Internet of things trusted group based on multidimensional attribute trust model

Accurate prediction of the trust relationship is the basis for trusted access and secure interaction between Internet of things nodes. To evaluate the degree of trust, a trust metric is assigned to every node depending on its several attributes. Normal nodes in Internet of things tend to suffer collusion attacks from malicious nodes; thus, the accuracy of the trust measurement decreases. To enhance the security of interaction between massive Internet of things nodes, we propose a multidimensional attribute trust model and a dynamic maintenance mechanism of a trusted group. The proposed model provides a reference for the selection and evaluation of node multidimensional attribute factors to adapt to different Internet of things application scenarios. The dispersion of satisfaction records is used to discover abnormal data and weaken its influence on the calculation of the node’s comprehensive trust evaluation. The construction of trusted groups provides an architectural foundation for the application of group signature that maintains low network overhead. The performance of multidimensional attribute trust model and dynamic maintenance mechanism is verified using Netlogo. Simulation results show the efficiency of the proposed model to classify the malicious nodes and honest nodes, as well as to build a trusted group that could ensure honest nodes occupy the major proportion.

Request/Advertise-Based Content Forwarding in Vehicular Named Data Networking

Vehicular named data networking (VNDN) is a new model for establishing communication between vehicles in vehicular ad hoc networks (VANETs). VNDN research is extensive, but a few challenges, such as those related to packet forwarding and node mobility, remain to be addressed. Forwarding packets on the basis of a broadcast strategy maximizes a communication channel. In addition, VNDN topology changes rapidly because of node mobility, and this situation interrupts the communication channels between vehicles. In this study, we present a request/advertise-based content forwarding (RACF) approach to improve VNDN forwarding performance and address the node mobility issue. In RACF, the content consumer broadcasts a request message when it fails to find the required content. The content provider can also advertise contents by broadcasting an advertisement message. We utilize several motion parameters, such as distance and direction, and calculate the link expiration time between vehicles using motion equations to build dynamic unicast forwarding paths. This approach gives the provider or consumer the opportunity to find the corresponding packet with a low delay and small network overhead. Simulation results show that RACF improves VNDN performance in terms of network overhead, interest satisfaction rate, and interest time delay.

A Study on the Concept of Using Efficient Lightweight Hash Chain to Improve Authentication in VMF Military Standard

Authentication algorithms in the form of cryptographic schemes, such as the Secure Hash Algorithm 1 (SHA-1) and the digital signature algorithm (DSA), specified in the current variable message format (VMF) military standard have numerous reliability-related limitations when applied to tactical data link (TDL) and multi-TDL networks (MTN). This is because TDL and MTN require maximum tactical security, communication integrity, and low network overhead based on many protocol header bits for rapid communication with limited network resources. The application of such authentication algorithms to TDL and MTN in a rapidly changing battlefield environment without reinforcement measures will lead to functional weaknesses and vulnerabilities when high-level digital-covert activities and deception tactics are implemented. Consequently, the existing VMF authentication scheme must be improved to secure transmission integrity, lower network transaction, and receive authentication tactical information in VMF-based combat network radio (CNR) networks. Therefore, in this study, a tactical wireless ad hoc network topology, similar to that of the existing CNRs, is considered, and a lightweight multi-factor hash chain-based authentication scheme that includes a time-based one-time password (T-OTP) for network overhead reduction and terminal authentication is proposed, coupled with exception handling. The proposed method enhances the confidentiality of tactical message exchanges and reduces unnecessary network transactions and transmission bits for authentication flows between real-time military terminals owned by squads, while ensuring robustness in limited battlefields. Based on these approaches, in the future, we intend to increase the authentication reliability between wireless terminals in the Korean variable message format (KVMF)-based CNR networks based on the Korean Army Corps network scenarios.

Distributed deduplication with fingerprint index management model for big data storage in the cloud

As data progressively grows within data centers, the cloud storage models face several issues while storing data and offers abilities needed to shift data in an adequate time frame. This study aims to develop a distributed deduplication model to achieve scalable throughput and capacity utilizing many data servers for duplicating data in parallel with minimal loss. This paper proposes a new cloud storage model based on a distributed deduplication with the fingerprint index management (DDFI) model. The DDFI model operates on three main stages. At the initial stage, the DDFI model makes use of an effective routing technique depending upon the similarity level of the data, which leads to low network overhead by rapid identification of storage locations. In the second stage, the duplicate data identification procedure is carried out by the use of the MD5 algorithm. At the final stage, a fingerprint index management process is executed where a fingerprint index comprises fingerprints and its corresponding position details of every written chunk. For optimizing the results of the deduplication performance, the DDFI model manages the fingerprint index in storage space and only sometimes writes to disk at the same time as the cloud database scheme is idle. The simulation outcome exhibited that the presented DDFI model offered maximum results with a higher deduplication ratio (DR) with a minimum overhead of network bandwidth. From the detailed comparative analysis, it is inferred that the presented DFFI model offered maximum relative DR, maximum duplication performance, minimum read bandwidth, and write bandwidth.

Routing Scheme Based on Community Correlation in Socially Aware Networking

Since most existing routings in socially aware networking only perform well in a certain statistic of network performance (e.g., Epidemic has high delivery rate but high network overhead, Direct Delivery has low network overhead but poor performance in latency and delivery rate), a new routing scheme: Community Correlation Based Routing (CCBR) is proposed in this paper to balance the delivery rate and network overhead. In CCBR, message forwarding is divided into two phases: in-community and across-community. We first define three new indicators by analyzing the social attributes of nodes: the social relationship between nodes, the active of node and the community correlation between communities. Then we use the three new indicators to form two functions: in-community forwarding utility and across-community forwarding utility. When messages are in the stage of in-community forwarding, we select the node with higher in-community forwarding utility as the relay node to make the messages deliver to the destination node faster. And when messages are in the across-community forwarding stage, the relay node with higher across-community forwarding utility is selected to avoid the messages being confined to the local community and make sure that the messages can always be transmitted to the destination community. Extensive simulations show that the proposed CCBR routing scheme can effectively improve the message delivery rate and greatly reduce the network overhead. And it can achieve better performance compared with the existing routing schemes even under the limited cache.

Towards a Smarter Data dissemination strategy in connected environments

Abstract Since the last decade, the emergence of affordable smart devices in web-connected environments (e.g., smart cities and buildings) has been a critical step towards improving user comfort. In this respect, notifying end-users about different essential events in their environment is of significant interest. Thus, the design of an efficient data dissemination strategy is of paramount importance. The thriving challenge would be to inform interested devices about an event and to avoid as far as possible the network collision problem. To overcome these limitations, we introduce a new generic and adaptive data Dissemination Strategy in Connected Environments (DSCE). Our strategy’s main thrust is reaching a high delivery ratio as well as a low network overhead by sending messages only to the interested devices. To do so, we rely on a semantic-based approach to define device interests and to identify salient messages. Moreover, to cope with the broadcast storm problem and to organize the scheduling of data dissemination, we provide a new waiting time measure for each device before rebroadcasting any received message. The performed experiments show that our strategy outperforms its competitors in terms of effectiveness and efficiency.

Cloud data acquisition and processing model based on blockchain

Data outsourcing has gradually become a mainstream solution, but once data is outsourced, data owners will without the control of the data hardware, there is a possibility that the integrity of the data will be destroyed objectively. Many current studies have achieved low network overhead cloud data set verification by designing algorithmic structures (e.g., hashing, Merkel verification trees); however, cloud service providers may not recognize the incompleteness of cloud data to avoid liability or business factors fact. There is a need to build a secure, reliable, non-tamperable, and non-forgeable verification system for accountability. Blockchain is a chain-like data structure constructed by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. Blockchain is a chain-like data structure constructed by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. This paper uses the Hadoop framework to implement data collection and storage of the HBase system based on big data architecture. In summary, based on the research of blockchain cloud data collection and storage technology, based on the existing big data storage middleware, a large flow, high concurrency and high availability data collection and processing system has been realized.

Attestation-enabled secure and scalable routing protocol for IoT networks

Cybercrime in the past decade has experienced an all-time high due to the inclusion of so-called smart devices in our daily lives. These tiny devices with brittle security features are often dubbed as the Internet of Things (IoT). Their inclusion is not only limited to our daily lives but also in different fields, for example, healthcare, smart-industries, aviation, and smart-cities. Although IoT devices make our lives easy and perform our jobs in a smart way, but their fragile security mechanisms pose a severe challenge regarding safety and privacy of its users. Attacks like Stuxnet, and Mirai-botnet are the key examples of the damages that can be caused by maliciously controlling these devices. One effective tool to identify a malicious entity at a network device is to perform Remote Attestation (RA). However, performing RA over a large, heterogeneous IoT network is difficult tasks due to resource constrain nature of these networks. To this end, we propose a novel scheme called SARP, which is an attestation-assisted secure and scalable routing protocol for IoT networks. SARP performs attestation in large scale IoT networks by using Routing Protocol for Low Power and Lossy Networks (RPL) framework and exploiting the inbuilt features of RPL. In particular, SARP uses attestation technique that not only secures the network from internal attacks, but it also provides security to RPL’s data communication process, which helps to improve the overall network performance. Moreover, SARP supports network mobility, device heterogeneity, and network scalability, while it does not sacrifice the key requirements of IoT networks such as low energy and memory consumption, and low network overhead. The simulation results obtained in different IoT scenarios in presence of various types of attacks show the effectiveness of SARP, concerning energy consumption, packet delivery ratio, network overhead, data integrity, and communication security.

TARE: Topology Adaptive Re-kEying scheme for secure group communication in IoT networks

Internet of Things (IoT) employs a large number of sensors and actuators to collect and act upon data for its smart functionalities. These devices are considered as a part of the Low-power and Lossy Networks due to their use of low power embedded hardware and computationally constrained nature. For synchronization and utility, these devices are often clubbed together logically to form groups. To maintain data confidentiality within a group, a shared symmetric key called the Group Key (GK) is used by all the group members. The GK must be redistributed upon joining and leaving of a group member to maintain forward and backward secrecy. However, the key management (i.e., generation and distribution) process causes overhead which consumes the scarce network resources. In this paper, we propose TARE, a novel Topology Adaptive Re-kEying (TARE) scheme for lightweight and secure group communication. TARE integrates the principles of routing tree mapped logical key tree and local derivation of the key over the an IPv6 Routing Protocol for low-power and Lossy networks in an original way. TARE takes into consideration the current routing topology and makes maximum energy reduction as the premise for its choice of key derivation and distribution methods, thus, it reduces the network energy consumption while maintaining key secrecy and data confidentiality. In particular, TARE provides the following advantages: (1) lower network overhead and bandwidth utilization in key management and re-distribution operations, (2) effective against the network mobility in scalable IoT networks, (3) secure group communications in network against attacks such as man-in-the-middle and eavesdropping, and (4) data confidentiality by ensuring backward and forward secrecy in key distribution method. We evaluate the performance of TARE and compare it with existing schemes. Our results show the effectiveness of TARE regarding energy consumption, bandwidth utilization, and the number of encrypted message transmissions during the re-keying operations.