Terms Of Communication Latency Research Articles

Cat and Mouse Based Task Optimization Model for Optimized Data Collection in Smart Agriculture

Data collection from agricultural fields is tiring and requires novel methodologies to produce reliable outcomes. The combination of edge and wireless sensor networks (WSN) for smart farming enabled the efficient collection of data from remote fields to a vast extent. Adopting an optimization algorithm to achieve the data collection task is prioritized in the proposed work, and a new and effective data collection framework is proposed. The proposed framework initially collects the data from the agricultural fields via sensors and then transmits it to the edge server. The path between the sensors and the edge server is optimally obtained using the cat and mouse based task optimization (CMTO) model. The sensed data are transmitted through the optimal route, and then the edge server obtains and evaluates the data based on the data quality metrics such as precision, correctness, completeness and reliability. The valid data are then identified and transferred to the cloud servers for storage. The simulation of the work is done in Python platform and evaluated using the crop recommender dataset. The evaluations proved the method's efficacy compared to the existing state-of-the-art algorithms. The proposed work also provided upto 12.5% of improvement in terms of energy consumption, 7.14% of improvement in terms of communication latency, 4% of improvement in terms of execution cost, 2.27% of improvement in terms of completeness, 1.12% of improvement in terms of precision, 9.52% of improvement in terms of correctness, and 3.37% of improvement in terms of reliability.

Virtualization Technology Blending for resource-efficient edge clouds

Edge computing brings virtualized services closer to users to improve their operation (e.g., in terms of communication latency, reliability, or data privacy) and is considered as a main technological enabler for 5G and beyond ecosystems. Edge cloud orchestration approaches are typically adapting server and network resources to rapid changes in the workload, mitigate resource exhaustion, or address service performance requirements. In this context, we introduce and investigate a new cloud orchestration strategy, called Virtualization Technology Blending (VTB). VTB considers edge clouds supporting alternative virtualization options, including multiple unikernel flavors and container builds of the same or a similar service, which can be effectively blended to handle cases with challenging resource requirements, since they exhibit diverse resource requirements and performance capabilities. Here, we provide an elaboration of VTB, backed by a relevant optimization framework, in order to validate its feasibility and assess its benefits. According to our results, the proposed strategy augments the number of users that can be served up to 74% and improves the server and network resource utilization up to 50.9% and 34.4%, respectively.

Auction based cost‐efficient resource allocation by utilizing blockchain in fog computing

AbstractIn the IoT‐cloud environment, the growing amount of spawn data may limit performance in terms of communication latency, network traffic, processing power, and energy usage. The introduction of fog computing extends the cloud services nearer to the edge of the network. Since these lightweight fog servers are not able to fulfill the demand of every user node and process each offloaded task due to the limited computation resources. Accordingly, an efficient resource management scheme is required to proficiently handle fog resources. The profit‐driven nature of both the fog service providers and user nodes increases the possibility of malicious activity while resource trading for their advantages or to privilege a bunch of devices. In this article, we designed a trusted and fair incentive mechanism that encourages buyers and sellers to trade by leveraging the benefits of blockchain and smart contracts. Especially, a combinatorial double auction employed market model is proposed which satisfies different economical properties such as individual rationality, budget balance, and truthfulness. Blockchain‐driven decentralized fog environments prevent the tampering of trade‐related information by the malicious nodes. Simulation results indicate that the proposed combinatorial double auction significantly improves the network utilization by improved winner determination and pricing model.

A novel proactive controller deployment protocol for 5G-enabled software-defined Vehicular Networks

With the vast expansion of data loads across numerous safety and infotainment applications, the software-defined paradigm has become a critical component for 5G-enabled and intelligent vehicular networks. By transforming the network’s rigid hardware devices into programmable units, SDVN offers a broad view of the entire network status and a standard interface between heterogeneous wireless access technologies. However, deploying a centralized control unit carries several problems, such as bottleneck issues and densification issues. A distributed control plane is a potential alternative to a centralized control plane. Still, it poses many questions about the optimal location of local control units and the number of control units required for a given network structure. In this article, we propose an efficient proactive controller deployment and assignment strategy for distributed 5G-enabled software-defined Vehicular Networks based on predictive vehicles’ traffic flow information and communication delays between switch-enabled roadside units. A thorough performance evaluation is conducted under different vehicular network’s mobility scenarios. We assess the performance of the proposed method in terms of communication latency and load on the subsequent control units and their assigned set of switches. The proposed proactive clustering method is compared to different forms of controller placement methods. The results showed that the proposed approach reduced the network’s communication delays while maintaining a balanced distributed load among controllers over the simulation time.

An edge-cloud collaborative computing platform for building AIoT applications efficiently

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), or AIoT, has breathed a new life into IoT operations and human-machine interactions. Currently, resource-constrained IoT devices usually cannot provide sufficient capability for data storage and processing so as to support building modern AI models. An intuitive solution is to integrate cloud computing technology into AIoT and exploit the powerful and elastic computing as well as the storage capacity of the servers on the cloud end. Nevertheless, the network bandwidth and communication latency increasingly become serious bottlenecks. The emerging edge computing can complement the cloud-based AIoT in terms of communication latency, and hence attracts more and more attention from the AIoT area. In this paper, we present an industrial edge-cloud collaborative computing platform, namely Sophon Edge, that helps to build and deploy AIoT applications efficiently. As an enterprise-level solution for the AIoT computing paradigm, Sophon Edge adopts a pipeline-based computing model for streaming data from IoT devices. Besides, this platform supports an iterative way for model evolution and updating so as to enable the AIoT applications agile and data-driven. Through a real-world example, we demonstrate the effectiveness and efficiency of building an AIoT application based on the Sophon Edge platform.

6G-enabled Edge Intelligence for Ultra -Reliable Low Latency Applications: Vision and Mission

The increasing demand for automation and instant solution leads the technological world towards massive applications such as the Internet of drones, Autonomous Vehicles (AVs), border surveillance, telesurgery, and Augmented Reality (AR), which requires a vast upgrade in technology with improved processing and computation capabilities. The Centralized Cloud Server (CCS) provides the facility to compute critical tasks at the central data server, but it consumes more time due to the distance between CSS and edge device. In this article, we discuss the advantages of edge computing over cloud computing to overcome latency and reliability issues in critical applications. Moreover, the idea of processing and analyzing massive applications at the edge comes up with the requirement of building intelligence at the edge to compute complex tasks within a negligible time. Edge intelligence offers intelligence at the edge to process large datasets for critical computations and to overcome storage issues. Also, the performance tolerant connectivity and low-speed rate issues with 4G and 5G can be solved using a 6G wireless network. The 6G connected edge intelligence application offers ultra-low-latency, security, and reliability mechanisms that could be helpful in COVID-19 pandemic situations. We have also discussed the demonstration of aforementioned massive application in the form of a case study on combating COVID-19 situations using 6G-based edge intelligence. The case study depicts the benefits of using 6G (latency: 10−100μs) over 4G (latency: <10ms) and 5G (latency: <5ms) communication networks. The proposed 6G-enabled scheme is compared against the traditional 4G and 5G networks to designate its efficiency in terms of communication latency and network mobility. Eventually, we then analyzed various open issues and research challenges in this emerging research area for future gains and insights.

Energy-Adaptive and Bottleneck-Aware Many-to-Many Communication Scheduling for Battery-Free WSNs

Battery-free wireless sensor networks (BF-WSNs) have captured the interest of research community in recent years. Compared with traditional battery-powered WSNs (BP-WSNs), BF-WSNs can prolong the lifetime of the network by exploiting ambient energy. Many-to-many communication is widely used in many applications of WSNs and the problem of minimum-latency many-to-many communication scheduling in BF-WSNs is of great significance. However, this problem has not been studied yet. The existing algorithms for BP-WSNs and BF-WSNs are not suitable for minimum-latency many-to-many scheduling problem in BF-WSNs. Also, different from BP-WSNs, energy-bottleneck nodes with low recharge rate and high workload in BF-WSNs make the problem more challenging. To address these issues, in this article, we first study the problem of many-to-many scheduling in BF-WSNs with the purpose of minimizing communication latency. The problem is formally defined and proved to be NP-hard. The energy-adaptive and bottleneck-aware scheduling algorithm for many to many in BF-WSNs is proposed. The correctness and average latency of the proposed algorithm are carefully analyzed. Extensive simulations show that our algorithm has high performance, in terms of communication latency and energy usage ratio. Furthermore, we also extend the proposed algorithm to other network models.

Arquitectura de comunicación para la digitalización de la agricultura en torno a la maquinaria agrícola

Se presenta una arquitectura de comunicación para habilitar servicios en la nube en torno a la maquinaria agrícola en zonas rurales con infraestructura de conectividad limitada. La arquitectura propuesta se compone de tres módulos, Unidad a bordo, gateway y servicio en la nube, que extienden el alcance de la cobertura de comunicación y permiten el intercambio de datos desde la maquinaria agrícola a la nube. La arquitectura se implementa con hardware de bajo costo y librerías de código abierto que permiten una rápida implementación. Los resultados obtenidos, en términos de latencia de comunicación, indican que la solución es adecuada para aplicaciones de monitoreo en maquinarias agrícola que utilizan ISOBUS.

Reliable and efficient hierarchical organization model for computational grid

Although the hierarchical model appears to be an effective solution to organize the resource managements in grid systems which have more stringent demand for both scalability and efficiency, it has some limitations which need to be addressed. For example, the master/manager resources in different levels represent single points of failure and they may be sources of bottleneck and communication overhead especially if they are not efficiently selected. Moreover, the dynamic and fault-prone nature of grids cannot be treated by static structures while the manual construction and repairing are also prohibitive due to the highly caused overhead which often represents a significant obstruction to an efficient resource utilization (especially for those with intermittent availabilities). The main objective of this paper is to first introduce a self-repairing n-try dynamic hierarchical grid model for scheduling and load balancing in which each master resource will be replicated on one of its children resources. Second, an efficient methodology to elect masters–replicas resources is proposed. In this methodology, the masters–replicas are selected based on both resource reliability (in terms of MTBF) and resource proximity from the other nodes in specified groups (in terms of communication latency). Validation of the proposed methodology based on the proposed model is done via simulation. Experimental results show that the proposed model has a great impact on the overall performance. Compared to other approaches, the simulations show that our approach decrements the average completion time (ACT) by 18.9%–25%, increases the tree stability ratio (TSR) up to 26.2%–27.1%, and minimizes the total communication overhead (TCO) by 4.4%–18.7% in the range of system parameter values examined.

The Implementation of a Low Cost Single-cycle On-chip Router Based on Multiple Virtual Output Queuing

Network-on-Chip (NoC) is becoming a popular solution for communication on System-on-Chips. A router is a major component of NoC which is responsible for handling the communication. Its architecture significantly impacts on the performance of NoC. In this paper, we propose a low latency router architecture based on virtual output queuing (VOQ). The number of pipeline stages of a packet transfer can be reduced to one stage, by using VOQ buffers and speculatively performing switch allocation and switch traversal in parallel. This paper also proposes a multiple VOQ architecture for which each input port maintains multiple queues for each output channel to improve the throughput of the router. We have implemented the proposed router on FPGA and evaluated in terms of communication latency, throughput and hardware amount. The experimental results show that in a 4 × 4 two-dimensional mesh network, the proposed multiple VOQ router reduces the communication latency by 25% and cost of area by 15.6% as compared to the look-ahead speculative virtual channel router.

An Operational Semantics for Shared Messaging Communication

Shared Messaging Communication (SMC) has been introduced in [Satya Kiran M.N.V., Jayram M.N., Pradeep Rao, and S.K. Nandy. A complexity effective communication model for behavioral modeling of signal processing applications. In Proceedings of 40th Design Automation Conference, 2003] as a model of communication which reduces communication costs (both in terms of communication latency and memory usage) by allowing tasks to communicate data through special shared memory regions. Sending a reference to an otherwise inaccessible memory regions rather than the data itself, the model combines the advantages of message passing and shared memories. Experimental results have shown that SMC in case of large data payloads clearly outperforms the classical message passing.In this paper we give a formal operational semantics to SMC exhibiting unambiguously the effect of executing an SMC command on local and shared memories. Based on this semantics we show that any program using message passing can be proved to be weakly bisimilar to one based on SMC and that with respect to communication costs the latter is amortised cheaper, [A. Kiehn and S. Arun-Kumar. Amortised bisimulations. In Proceedings of FORTE 2005, number 3731 in Lecture Notes in Computer Science, pages 320–334. Springer-Verlag, 2005].

Modeling and evaluation of a new message-passing system for parallel multiprocessor systems

As parallel implementation of complex applications is becoming popular, the need for a high performance interprocessor communication system becomes imminent, especially in loosely coupled distributed-memory multiprocessor networks. An important factor in the efficiency of these networks is the effectiveness of the message-passing system which manages the data exchanges among the processors of the network. This paper presents the modeling and performance evaluation of a new Message-Passing System (MPS) for distributed multiprocessor networks without shared-memory and where the processors or Processing Elements (PEs) are connected to each other by point-to-point communication links. For maximum performance, the MPS manages the communication and the synchronization between the different tasks of an application by means of three approaches. One is an asynchronous send/receive approach which handles efficiently server like tasks, the second is a synchronous send/receive approach which handles efficiently streaming communication mode and the third is a virtual channel approach which minimizes the overhead of the synchronization mechanism, efficiently handling the burst mode of heavy communication between tasks. The developed models of the MPS approaches enable the determination of analytical expressions for different performances and a comparison between analytical and experimental performances reveals that the models predict the MPS performance with high accuracy. The MPS written in Parallel ANSI C, is studied on a mesh topology network of 16 transputers T800. The MPS performances for each approach are studied and presented in terms of communication latency, throughput, computation efficiency and memory consumption.

Inter Nepcon—Assembly and repair of multi-chip modules : A. G. Cozens, Electron. Engng, October (1969), p. 22

Although the hierarchical model appears to be an effective solution to organize the resource managements in grid systems which have more stringent demand for both scalability and efficiency, it has some limitations which need to be addressed. For example, the master/manager resources in different levels represent single points of failure and they may be sources of bottleneck and communication overhead especially if they are not efficiently selected. Moreover, the dynamic and fault-prone nature of grids cannot be treated by static structures while the manual construction and repairing are also prohibitive due to the highly caused overhead which often represents a significant obstruction to an efficient resource utilization (especially for those with intermittent availabilities). The main objective of this paper is to first introduce a self-repairing n-try dynamic hierarchical grid model for scheduling and load balancing in which each master resource will be replicated on one of its children resources. Second, an efficient methodology to elect masters–replicas resources is proposed. In this methodology, the masters–replicas are selected based on both resource reliability (in terms of MTBF) and resource proximity from the other nodes in specified groups (in terms of communication latency). Validation of the proposed methodology based on the proposed model is done via simulation. Experimental results show that the proposed model has a great impact on the overall performance. Compared to other approaches, the simulations show that our approach decrements the average completion time (ACT) by 18.9%–25%, increases the tree stability ratio (TSR) up to 26.2%–27.1%, and minimizes the total communication overhead (TCO) by 4.4%–18.7% in the range of system parameter values examined.