Data Delivery Latency Research Articles

Optimized Wireless Visual Sensor Networks for Guaranteed Target Coverage With Maximum Lifetime and Least Data Delivery Latency

Optimized Wireless Visual Sensor Networks for Guaranteed Target Coverage With Maximum Lifetime and Least Data Delivery Latency

An Energy-Efficient Routing Protocol with Reinforcement Learning in Software-Defined Wireless Sensor Networks.

The enormous increase in heterogeneous wireless devices operating in real-time applications for Internet of Things (IoT) applications presents new challenges, including heterogeneity, reliability, and scalability. To address these issues effectively, a novel architecture has been introduced, combining Software-Defined Wireless Sensor Networks (SDWSN) with the IoT, known as the SDWSN-IoT. However, wireless IoT devices deployed in such systems face limitations in the energy supply, unpredicted network changes, and the quality of service requirements. Such challenges necessitate the careful design of the underlying routing protocol, as failure to address them often results in constantly disconnected networks with poor network performance. In this paper, we present an intelligent, energy-efficient multi-objective routing protocol based on the Reinforcement Learning (RL) algorithm with Dynamic Objective Selection (DOS-RL). The primary goal of applying the proposed DOS-RL routing scheme is to optimize energy consumption in IoT networks, a paramount concern given the limited energy reserves of wireless IoT devices and the adaptability to network changes to facilitate a seamless adaption to sudden network changes, mitigating disruptions and optimizing the overall network performance. The algorithm considers correlated objectives with informative-shaped rewards to accelerate the learning process. Through the diverse simulations, we demonstrated improved energy efficiency and fast adaptation to unexpected network changes by enhancing the packet delivery ratio and reducing data delivery latency when compared to traditional routing protocols such as the Open Shortest Path First (OSPF) and the multi-objective Q-routing for Software-Defined Networks (SDN-Q).

An efficient adaptive cache management scheme for named data networks

With the advent of modern technologies and applications, NDN networks have been known as a reliable approach to meeting the needs ahead. A prominent feature of these networks is the ability to cache comprehensive content within network nodes. Separating the content from the original location reduces the content retrieval latency, network traffic, and overhead within the applications containing considerable required data volume. This approach also improves the user experience. In this article, first, the idea of intra-network caching with an approach based on the cooperation of adjacent nodes along the path is proposed to form a local virtual cluster with distributed cache space. Second, popular cached data will be shared by exchanging minimal notification messages between them to develop content storage decisions. Two new modules are defined in each node, called RIT and NCT which have offered the possibility of utilizing the resources of nodes adjacent to the delivered route. In the next stage, a novel approach is proposed to determine the content caching threshold in each node by considering the parameters affecting the content, as well as the network topology and real-time status of the nodes. Finally, the decision mechanism regarding the storage of content in each node along the path is presented by using an adaptive approach based on set thresholds. In such a manner, undesirable redundancy of content that wastes network resources will be removed, and more caching space in each area of the network will be provided. The results of simulations using ndnSim revealed that the performance of the proposed algorithm (cache hit ratio, average data delivery latency and path stretch) is better than other existing benchmark strategies. Considering other different parameters confirmed the effectiveness of the presented approach.

Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment

The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network periphery that are distributed and resource-constrained. On the other hand, microservice architectures are becoming increasingly popular for developing IoT applications owing to their maintainability and scalability advantages. Providing an efficient communication medium for large-scale microservice-based IoT applications constructed from small and independent services to cooperate to deliver value-added services remains a challenge. This paper introduces an event-driven communication medium that takes advantage of Edge–Cloud publish/subscribe brokers for microservice-based IoT applications at scale. Using the interaction model, the involved microservices can collaborate and exchange data through triggered events flexibly and efficiently without changing their underlying business logic. In the proposed model, edge brokers are grouped according to their similarities in event channels and the proximity of their geolocations, reducing the data delivery latency. Moreover, in the proposed system a technique is designed to construct a broker-based utility matrix with constraints in order to strike a balance between delay, relay traffic, and scalability while arranging brokers into proper clusters for efficient data delivery. Rigorous simulation results prove that the proposed publish/subscribe model can provide an efficient interaction medium for microservice-based IoT applications to collaborate and exchange data with low latency, modest relay traffic, and high scalability at scale.

An enhanced ACO-based mobile sink path determination for data gathering in wireless sensor networks

By facilitating the data delivery in wireless sensor networks, the movement of mobile sink can enhance the network connectivity and sensory coverage. However, the optimal path determination of mobile sink is a NP-hard optimization problem. By jointly considering the cluster-based routing and sink mobility, this paper proposes an enhanced ACO-based movement scheduling of mobile sink for data gathering in wireless sensor networks. To meet the delay requirements and balance the energy consumption of the sensor nodes, the optimal cluster heads selection is introduced. Then, an enhanced ACO-based movement scheduling algorithm is proposed to obtain the shortest path of mobile sink by traversing the network. The simulation results show that our proposed method can offer a promising performance in terms of reducing data delivery latency and extending the lifetime of the network.

Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network

Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times. We solve this problem via a novel expediting beam selection by leveraging multimodal data collected from sensors like LiDAR, camera images, and GPS. We propose individual modality and distributed fusion-based deep learning (F-DL) architectures that can execute locally as well as at a mobile edge computing center (MEC), with a study on associated tradeoffs. We also formulate and solve an optimization problem that considers practical beam-searching, MEC processing and sensor-to-MEC data delivery latency overheads for determining the output dimensions of the above F-DL architectures. Results from extensive evaluations conducted on publicly available synthetic and home-grown real-world datasets reveal 95% and 96% improvement in beam selection speed over classical RF-only beam sweeping, respectively. F-DL also outperforms the state-of-the-art techniques by 20-22% in predicting top-10 best beam pairs.

Optimal Distributed MQTT Broker and Services Placement for SDN-Edge Based Smart City Architecture

A smart city is an urban area that collects data from various devices to effectively manage urban resources. The smart city IoT infrastructure connects numerous devices to an Internet-protocol-based low-power wireless network, shares massive amounts of data, and facilitates the development of new services. Message queuing telemetry transport (MQTT), a lightweight exchange protocol for the IoT environment, uses a publish and subscribe structure via a centralized broker to share data. The extent of edge computing provides distributed and closer resources to the data source while maintaining low transmission costs. However, a centralized MQTT data broker is unsuitable for distributed edge resources and could result in high latency, traffic, and bottleneck risk. Therefore, we proposed a distributed MQTT broker optimized architecture. A distributed MQTT broker for edge resources could reduce network traffic and data delivery latency by only managing consumed topics in the network. We formulate an integer non-linear program to optimize container placement and avoid wasting edge computing resources. We compared our proposed architecture to the existing distributed MQTT middleware architecture with greedy and random container placement through extensive simulation. Our methods show better performance in lowering deployment failure ratio, power consumption, network usage, and synchronization overhead.

Fuzzy-Based Multi-Layered Clustering and ACO-Based Multiple Mobile Sinks Path Planning for Optimal Coverage in WSNs

Wireless Sensor Network with Mobile Collectors (MCs) enable increased flexibility and convenience in data gathering for numerous large-scale applications. However, introducing MCs also brings a new set of challenges to overcome. To reduce the data delivery latency of the application, it is required to select the minimum number of Rendezvous Points (RPs) that allow most sensors to forward data in single-hop and best path must be planned for each MC to provide uniform path length and round-trip time for all MCs. In contrast to existing schemes, we propose a Fuzzy C-Means based multi-layered RP Clustering and ACO-based Route-Planning scheme (FCM-RP) which is a robust method to determine RP-positions and MC-assignments. More particularly, the existing works have given priority to either reducing network energy consumption or minimizing data gathering delay. In our paper, along with these priorities, we consider the robustness and adaptability of the algorithm; meaning that, our algorithm can modify the planned trajectories of deployed MCs in response to sensor node failures. This allows to adapt to any changes in network topology caused by node failures or external factors. Also, to maximize utilization of each RP and provide greater coverage for nodes, the ideal positioning of RPs with minimum coverage overlap with neighboring RPs is considered. Simulation analysis shows that the FCM-RP scheme defines better routes for MCs in terms of total path length and delay, and significantly outperforms the existing algorithms in terms of total energy consumption and network lifetime.

Game theory based spectrum sensing and transmission in energy harvesting hybrid cognitive radio networks (EH‐HCRN)

SummaryIn an energy harvesting cognitive radio network (EH‐CRN), the correspondence among the primary transmitter (PT) and primary receiver (PR) for spectrum sensing and transmission becomes ineffective as a result of insufficient SNR, finite sensing duration and interferences. We recommend designing a suitable spectrum sensing structure based on game theory for energy harvesting hybrid CRN to resolve these complications. This comprises three phases: (i) co‐operative relay approach for primary user (PU) transmission; (ii) optimal sensing interval estimation; and (iii) co‐operative energy efficient spectrum sensing and transmission. Initially, a batch of suitable secondary transmitters (STs) are identified as relay nodes for the PTs in the first phase. Next, in the second phase, a sensing interval that maximizes a utility function devised concerning average throughput, collision probability regarding secondary user (SU) and energy detector threshold is determined by applying the game theory model. Finally, co‐operative transmission is utilized in the third phase if the PR could not decode the signal from PT via STs. The presented framework is simulated using NS2, and its efficiency is estimated in terms of the parameters like transmission latency, packet loss, residual energy and possibilities of successful data delivery.

Plane sweep algorithms for data collection for energy harvesting wireless sensor networks using mobile sink

Usage of mobile sink(s) for data gathering in wireless sensor networks(WSNs) improves the performance of WSNs in many respects such as power consumption, lifetime, etc. In some applications, the mobile sink $MS$ travels along a predefined path to collect data from the nearby sensors, which are referred as sub-sinks. Due to the slow speed of the $MS$, the data delivery latency is high. However, optimizing the {\em data gathering schedule}, i.e. optimizing the transmission schedule of the sub-sinks to the $MS$ and the movement speed of the $MS$ can reduce data gathering latency. We formulate two novel optimization problems for data gathering in minimum time. The first problem determines an optimal data gathering schedule of the $MS$ by controlling the data transmission schedule and the speed of the $MS$, where the data availabilities of the sub-sinks are given. The second problem generalizes the first, where the data availabilities of the sub-sinks are unknown. Plane sweep algorithms are proposed for finding optimal data gathering schedule and data availabilities of the sub-sinks. The performances of the proposed algorithms are evaluated through simulations. The simulation results reveal that the optimal distribution of data among the sub-sinks together with optimal data gathering schedule improves the data gathering time.

Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering

Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud publish/subscribe brokers model using an efficient two-tier routing scheme to alleviate these issues when transmitting event notifications in wide-scale IoT systems. In this model, IoT devices take advantage of proximate edge brokers strategically deployed in edge networks for data delivery services in order to reduce latency. To deliver data more efficiently, we propose a proactive mechanism that applies collaborative filtering techniques to efficiently cluster edge brokers with geographic proximity that publish and/or subscribe to similar topics. This allows brokers in the same cluster to exchange data directly with each other to further reduce data delivery latency. In addition, we devise a coordinative scheme to help brokers discover and bridge similar topic channels in the whole system, informing other brokers for data delivery in an efficient manner. Extensive simulation results prove that our model can adeptly support event notifications in terms of low latency, small amounts of relay traffic, and high scalability for large-scale, delay-sensitive IoT applications. Specifically, in comparison with other similar Edge-Cloud approaches, our proposal achieves the best in terms of relay traffic among brokers, about 7.77% on average. In addition, our model’s average delivery latency is approximately 66% of PubSubCoord-alike’s one.

Quick Convex Hull-Based Rendezvous Planning for Delay-Harsh Mobile Data Gathering in Disjoint Sensor Networks

Sink mobility is a significant technique to improve the performance of wireless sensor networks (WSNs). Generally a mobile sink visits several rendezvous points (RPs), forming a trip tour for data collection. However, the low movement speeds of mobile sinks tend to incur serious data delivery delays. In this article, we propose a quick convex hull-based rendezvous planning (QCHBRP) scheme, which aims to not only achieve full connectivity for disjoint WSNs but also construct a shorter trip tour and minimize the data delivery latency accordingly. The trajectory formation of the mobile sink is based on a path skeleton, i.e., a near-convex hull, which is created by the quick determination of several special locations as RPs. The benefits of QCHBRP are threefold. First, it is especially designed for disjoint WSNs where sensor nodes are deployed in multiple isolated segments and the network connectivity is lost in advance. Second, it is suitable for delay-harsh applications which require short paths of the mobile sink. Third, it is of much lower computational complexity compared with existing methods. The extensive analysis and experiments validate the effectiveness and advantages of this new scheme in terms of connectivity cost and data delivery delay.

Identification of optimal location points for efficient data gathering in IoT environment

SummaryRecent development in sensor technologies encourages the adoption of mobile data collectors (MDCs), that is, vehicles or robots, to collect data in the Internet of Things (IoT) environment. However, the slow travel speed of the MDCs leads to significant delays in the transmission of data. This work proposes an optimal region location point based route planning (ORLP‐RP) scheme to construct a shorter tour with minimized data delivery latency. Primary goal of the work is to distinguish the overlapped wireless sensor network (WSN) clusters and specify the overlapped regions to find optimal location points within each identified region to ensure one‐hop data gathering from each cluster. Thereafter, MDC trajectory is formed by using the nearest‐neighbor (NN) heuristic approach that generates a near optimal solution. In performance evaluation, the ORLP‐RP scheme has been compared with the existing algorithms such as EAPC, RDP, MOPSO, and NDCMC for data collection in terms of the number of optimal location points, total path length, data gathering latency, network lifespan, energy consumption, and data gathering ratio. Simulation results demonstrate the effectiveness of the proposed approach by improving the number of optimal location points with an average up to 58%, total path length improvement by 17%, energy consumption and network lifespan improvement on average by 18% and 12%, data gathering ratio by the MDC up to 9%, and reducing the delay of the MDC path by 21% compared with considered approaches. Thus, simulation results indicate better performance of the proposed scheme at all the indices listed above.

An Enhanced Replica Selection Approach Based on Distance Constraint in ICN

Fifth generation (5G) networks have a high requirement for low latency of data delivery. Information-centric networking (ICN) adopts the paradigm of separation of the identifier and locator. It is efficient in content distribution by supporting in-network caching and has the potential to satisfy the low latency requirement in 5G. Replica selection is a key problem to retrieving content in ICN. Prior research usually utilizes the nearest replica. However, using the nearest replica cannot guarantee the smallest content download delay. To exploit in-network caching better, we propose an enhanced replica selection approach, called ERS. ERS first uses a distance-constrained-based name resolution system to discover the nearby replicas. Then, the most appropriate replica is chosen according to a local state table that maintains the state of replica nodes within a limited domain. In addition to network distance and replica node load, ERS innovatively introduces the path congestion degree between requester and replica nodes to assist replica selection. With extensive simulations, the proposed approach shows better performance than the state-of-the-art methods in terms of average content download delay. Finally, the overhead of the proposed method is analyzed.

Machine Learning Based Detection and a Novel EC-BRTT Algorithm Based Prevention of DoS Attacks in Wireless Sensor Networks

Wireless sensor network allows nodes to communicate with another using wireless channels so that it is necessary for packet delivery to the nodes. Black hole attack occurs when a malicious node receives and misleads a set of nodes in the wireless network to drop the data they received. Wormhole attack occurs when malicious nodes create data delivery latency between two nodes in the wireless network. In this paper, we propose a Machine Learning based Naive Bayes Classifier to detect and Enhanced Code-based Round Trip Time based method prevent those two vital attacks. The black hole attack can be analyzed using this technique with the help of authorize code. Similarly, the wormhole attack and fake destination attack can be prevented by trip time to find the data delay time to reach the destination. In the case of black hole attack, a node which does not have authorized code will be considered as malicious node and discarded. If the packet does not arrive at the destination within the specific time, it will automatically detect the occurrence of wormhole in the wireless network. The proposed method is experimentally validated on network simulator (NS2). The main advantage of the proposed method is to reduce the communication overhead.

An optimal mobile sink sojourn location discovery approach for the energy-constrained and delay-sensitive wireless sensor network

Latest studies have exploited sink mobility as a pervasive approach to effectively mitigate the energy hole problem frequently perceived in the clustered wireless sensor network. For mobile sink (MS) based data collection, the selection of MS sojourn location plays a significant role in network performance. Moreover, there are several challenges to WSN in the form of energy preservation and data delivery latency. A one-hop data collection by the MS from each cluster conserves the network energy significantly but increases the number of sojourn points (SPs) which elongates the MS tour time. On the contrary, a lesser number of SPs may indulge the long chain of multi-hop data forwarding, which may further bring the energy hole issue into existence. In order to find a trade-off between energy preservation and data delivery delay, this article aims to attain an optimal number of SPs that guarantee the one-hop data collection from each cluster. Subsequently, an MS traversal path is formed by exploiting the ant colony optimization (ACO) algorithm that generates a near-optimal solution. Simulation results manifest the efficacy of the proposed protocol over the existing ones in terms of network lifetime, energy consumption, packet delivery ratio, end-to-end delay, etc.

A Lightweight Anonymous Device Authentication Scheme for Information-Centric Distribution Feeder Microgrid

Distribution feeder microgrid (DFM) built based on existing distributed feeder (DF), is a promising solution for modern microgrid. DFM contains a large number of heterogeneous devices that generate heavy network traffice and require a low data delivery latency. The information-centric networking (ICN) paradigm has shown a great potential to address the communication requirements of smart grid. However, the integration of advanced information and communication technologies with DFM make it vulnerable to cyber attacks. Adequate authentication of grid devices is essential for preventing unauthorized accesses to the grid network and defending against cyber attacks. In this paper, we propose a new lightweight anonymous device authentication scheme for DFM supported by named data networking (NDN), a representative implementation of ICN. We perform a security analysis to show that the proposed scheme can provide security features such as mutual authentication, session key agreement, defending against various cyber attacks, anonymity, and resilience against device capture attack. The security of the proposed scheme is also formally verified using the popular AVISPA (Automated Validation of Internet Security Protocols and Applications) tool. The computational and communication costs of the proposed scheme are evaluated. Our results demonstrate that the proposed scheme achieves significantly lower computational, communication and energy costs than other state-of-the-art schemes.

Latency of Waveform Data Delivery from the Southern California Seismic Network during the 2019 Ridgecrest Earthquake Sequence and Its Effect on ShakeAlert

Abstract The occurrence of the 4–6 July 2019 Mw 6.4 and Mw 7.1 Ridgecrest earthquake sequence provided the first full-scale test of the network and telemetry readiness of the Southern California Seismic Network (SCSN), to support the ShakeAlert earthquake early warning (EEW) system in California. ShakeAlert is a U.S. Geological Survey (USGS)-led collaboration to detect earthquakes and, when possible, to alert the public before the arrival of the strongest shaking. The SCSN performed well in its regional monitoring role for both the 4 July Mw 6.4 and the 6 July Mw 7.1 earthquakes. In the EEW role, it provided timely delivery of 5 s of P-wave data to ShakeAlert, which issued its first alert 6.9 s after origin time. Data delivery at peak data volumes for many stations exhibited some latency, and, as a consequence, some data arrived too late for analysis by one of the EEW algorithms. We find that the average link bandwidth for each station was sufficient, because all waveform data were delivered automatically to the archive, but link capacity for many stations was insufficient for peak demand. We describe the performance of the data telemetry for the sequence, including cellular, radio, hybrid, and backhaul systems. Cellular-based telemetry systems maintained low latency throughout strong shaking and after, but some stations, even at great distances, experienced subsequent brief increases in latency. Performance of radio links depended mostly on the signal strength of the link, with short-distance direct shots to high-bandwidth backhaul systems showing no latency impact, whereas stations on some long distance or marginal quality links suffered latencies of tens or hundreds of seconds. Improvements are being implemented to move telemetry links onto USGS and partner high-bandwidth microwave systems, and to reduce dependency on less robust long-distance radio shots.

Road-safety data delivery in IP-based vehicular sensor networks

ABSTRACT With the substantial increase in vehicle population, the road safety problems such as accidents have become serious. In this paper, we propose a road-safety data delivery solution in vehicular sensor networks to solve road safety problems and improve driving safety. The road-safety data delivery in vehicular sensor networks is delay-sensitive, so this solution aims to reduce the road-safety data delivery latency by shortening the addressing, routing and handover delays. In the proposal, a new address structure is proposed to achieve the road-safety data delivery. Based on the new address structure, the address configuration can be achieved without duplicate address detection and the routing can be performed without route discovery. Moreover, multiple sensor devices can achieve handovers via one handover operation without performing time-consuming care-of address configuration. Finally, the proposal is analyzed and evaluated, and the data shows that the proposal effectively reduces the road-safety data delivery latency.

An energy and coverage sensitive approach to hierarchical data collection for mobile sink based wireless sensor networks

In recent years, wireless sensor networks experience the energy hole problem as the most critical issue due to the heavy data forwarding load on the proximate sensor nodes to the sink. The best known solution found by the current state-of-the-art approaches for the energy hole problem is the Mobile Sink (MS) strategy. However, allowing the MS to visit every node for data collection incurs high data delivery latency, which may not be feasible in delay-sensitive applications. Thus, in this paper, restricted mobile sink motion is considered, where the MS halts at a limited number of locations stated as sojourn locations and all nodes disseminate their data to the nearby sojourn locations. The data dissemination to the sojourn location is achieved via a cluster-based routing protocol which aims to preserve the sensor nodes’ energy to enhance the network lifetime. Furthermore, analogous to network lifetime, extending the coverage lifetime is of equal importance in many coverage sensitive applications of WSN. Thus, this article incorporates the coverage parameter to the proposed protocol in order to preserve the network coverage despite certain nodes die. Based on the sojourn locations, the proposed routing algorithm ensures that each cluster data is disseminated to the MS following the minimum hop path to limit the data delivery delay. Experimental results demonstrate the efficacy of the proposed protocol over several state-of-the-art protocols with respect to different metrics like network lifetime, coverage ratio, energy efficiency, packet delivery ratio, end-to-end delay, etc.