Low Data Delivery Research Articles

Design model of an IEEE 802.11ad infrastructure for TSN-based industrial applications

This paper addresses two main aspects related to the application of the IEEE 802.11ad technology in an industrial environment, i.e., to guarantee a low latency and reliable data delivery while reducing the number of the deployed Access Points (APs). First, we discuss the parameter settings of the IEEE802.11ad MAC which guarantee a low delay by exploiting the synchronous service periods (SPs) mechanism. Then, we propose a binary linear programming model to enhance the reliability of the system under the Frame Replication and Elimination for Reliability (FRER). The model minimizes the number of deployed APs by taking into account the constraints on the available synchronous SPs. We also propose two heuristics, based on the described mathematical model, in order to efficiently solve the design problem, then compare the computational performance of the proposed approaches. The reported computational results highlight the complementary features of the compared methods and provide useful guidelines about the usage of the proposed approaches.

An ICN-Based IPFS High-Availability Architecture

The Interplanetary File System (IPFS), a new type of P2P file system, enables people to obtain data from other peer nodes in a distributed system without the need to establish a connection with a distant server. However, IPFS suffers from low resolution efficiency and duplicate data delivery, resulting in poor system availability. The new Information-Centric Networking (ICN), on the other hand, applies the features of name resolution service and caching to achieve fast location and delivery of content. Therefore, there is a potential to optimize the availability of IPFS systems from the network layer. In this paper, we propose an ICN-based IPFS high-availability architecture, called IBIHA, which introduces enhanced nodes and information tables to manage data delivery based on the original IPFS network, and uses the algorithm of selecting high-impact nodes from the entitled network (PwRank) as the basis for deploying enhanced nodes in the network, thus achieving the effect of optimizing IPFS availability. The experimental results show that this architecture outperforms the IPFS network in terms of improving node resolution efficiency, reducing network redundant packets, and improving the rational utilization of network link resources.

HOPASS: A two-layer control framework for bandwidth and delay guarantee in datacenters

In data center networks (DCNs), flows with different objectives coexist and compete for limited network resources (such as bandwidth and buffer space). Without harmonious resource planning, chaotic competition among these flows would lead to severe performance degradation. Furthermore, low latency is critical for many emerging applications such as augmented reality(AR), virtual reality(VR) and telepresence, making the network control problem even more challenging. To address the above issues, this paper novelly proposes a receiver-driven two-layer control framework called HOPASS, which incorporates a slow control layer and a fast control layer to strike a balance among multiple network sharing objectives and achieve low latency. The slow control layer ensures bandwidth guarantee in an aggregated flow level by solving a multi-objective network utility maximization (NUM) problem using an online learning approach. Then the results will be dispatched to the data plane by configuring weights in the switches with weighted fair queue functionality. Under the configuration dictated by the slow control layer, the fast control layer leverages the token packets sent by receivers to dynamically probe and reserve network capacity, so that it can proactively prevent network congestion and guarantee low latency data delivery. To evaluate the proposed framework, we have implemented HOPASS in ns-3 and conduct extensive experiments under various network scenarios. The simulation results show that HOPASS achieves near-optimal performance in terms of bandwidth allocation in multi-objective scenarios and also guarantees low end-to-end delay. Moreover, it outperforms DCTCP and NewReno in terms of average bandwidth utilization and global total network utility at the aggregated flow level. Therefore, we conclude that HOPASS provides an effective framework for DCNs when considering both multi-objective optimization and a low latency network.

APSSR: Adaptive Packet Size Selection Based Routing Protocol for Underwater Acoustic Sensor Networks

Underwater Acoustic Sensor Networks offer very promising solutions to monitor the aqueous environments. Due to the distinctive characteristics of UASNs, it is very challenging to design a routing protocol that can achieve maximum data delivery ratio in the network. The main challenge is the communication medium (acoustic links) that is subject to temporary attenuation and high bit error rate (BER), which limits the throughput efficiency of the Network. Besides this, another major issue is the continuous movement of nodes due to water currents and the availability of limited resources. Due to nodes mobility distance among sensor nodes and consequently, BER varies, which have a direct impact on packet size, hence, leads to high packet loss and low data delivery ratio. To achieve a high data delivery ratio, the selection of optimal packet size is of utmost importance. Consequently, the selection of next-hop forwarding node based on optimal packet size is needed. Therefore, in this paper, we propose an adaptive routing protocol named Adaptive Packet Size Selection Based Routing (APSSR) Protocol for UASNs. APSSR determines the optimal packet size adaptively based on both varying distances between sensor nodes and BER and selects the next hop based on optimal packet size and BER. The simulation results show greater network performance in terms of Network Lifetime, Data Reception Ratio at Sink node, Average Network Delay, Packet Reception Ratio, and Packets Drop Ratio

Design of an Efficient RPL Objective Function for Internet of Things Applications

Over the decade, a rapid growth in use of smart devices connected and communicating over the Internet is seen in various domains. The IPv6 Routing protocol for Low power and Lossy Networks (RPL) is the routing backbone of such IOT networks. RPL is a proactive, distance vector protocol which constructs the routes based on an Objective Function. The performance of RPL protocol largely depends on the design of Objective Function. Depending on application requirements, the RPL standard offers flexibility in design of the objective function and scope for improving the routing process. In this paper, an efficient Objective Function, RPL-FZ, is proposed. Speedy communication across nodes, low energy consumption and reliable data delivery is key to achieve quality of Service. Considering this, RPL-FZ uses relevant metrics like Residual Energy of Node, Delay and ETX (Expected Transmission count) to make the routing decisions. The metrics are combined using fuzzy logic technique to obtain a single metric Quality for each neighbor node. The neighbor with highest value of Quality is chosen as best parent to forward sensed data toward the collection unit. The proposed objective function RPL-FZ is integrated in the Contiki OS and network simulations are performed using the COOJA simulator. The performance evaluation reveals that RPL-FZ achieves 7% higher Packet Delivery rate, 8% lower energy consumption and 8% lesser latency as compared to single metric based standard objective functions OF0 and MRHOF.

Lightweight route adjustment strategy for mobile sink wireless sensor networks

<span>As a result of numerous applications and low installation costs, wireless sensor networks (WSNs) have expanded excessively. The main concern in the WSN environment is to lower energy consumption amidst nodes while preserving an acceptable level of service quality. Using multi-mobile sinks to reduce the nodes' energy consumption have been considered as an efficient strategy. In such networks, the dynamic network topology created by the sinks mobility makes it a challenging task to deliver the data to the sinks. Thus, in order to provide efficient data dissemination, the sensor nodes will have to readjust the routes to the current position of the mobile sinks. The route re-adjustment process could result in a significant maximization in the communication cost, which boosts the total energy depletion. This paper proposes a lightweight routes re-adjustment strategy for mobile sink wireless sensor networks (LRAS-MS) aimed at minimizing communication cost and energy consumption by reducing route re-adjustment in a cluster-based WSN environment. The simulation results show a significant reduction in communication costs and extending the network lifetime while maintaining comparable low data delivery delay. </span>

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.

Internet of things for healthcare monitoring applications based on RFID clustering scheme

COVID-19 surprised the whole world by its quick and sudden spread. Coronavirus pushes all community sectors: government, industry, academia, and nonprofit organizations to take forward steps to stop and control this pandemic. It is evident that IT-based solutions are urgent. This study is a small step in this direction, where health information is monitored and collected continuously. In this work, we build a network of smart nodes where each node comprises a Radio-Frequency Identification (RFID) tag, reduced function RFID reader (RFRR), and sensors. The smart nodes are grouped in clusters, which are constructed periodically. The RFRR reader of the clusterhead collects data from its members, and once it is close to the primary reader, it conveys its data and so on. This approach reduces the primary RFID reader’s burden by receiving data from the clusterheads only instead of reading every tag when they pass by its vicinity. Besides, this mechanism reduces the channel access congestion; thus, it reduces the interference significantly. Furthermore, to protect the exchanged data from potential attacks, two levels of security algorithms, including an AES 128 bit with hashing, have been implemented. The proposed scheme has been validated via mathematical modeling using Integer programming, simulation, and prototype experimentation. The proposed technique shows low data delivery losses and a significant drop in transmission delay compared to contemporary approaches.

Mobility and energy prediction models: Approach toward effective route management in mobile wireless sensor networks

AbstractMobile wireless sensor network (MWSN), as a backbone of most communication and embedded systems, is characterized by low data delivery as a result of the energy constraint and in‐deterministic movement of the nodes. To select optimal route path for a reliable data routing, protocols need to select neighbouring nodes with low mobility and residual energy parameters along the route. However, obtaining these parameters from the targeted nodes may encourage mischievous nodes to manipulate their energy and mobility parameters in their favor, leading to the selection of non‐optimal route path and/or exposing the network to other attacks. Rather than depending on the targeted nodes for these parameters, they can be accurately and securely obtained by sink node or base station using effective predictive models. However, most of the existing predictive models engaged by MWSN routing protocols either provide only the energy level or mobility factor of the nodes for optimal route path selection. This, therefore, reduces the reliability of the obtained optimal route paths in terms of data delivery. In this article, we propose predictive models for an accurate prediction of mobility and residual energy parameters of nodes in MWSN for efficient route management. The models are simulated on an existing MWSN protocol to determine their performance. The simulation results show that the adoption of these models in the development of routing protocol will not only lead to optimal route paths' selection but also enhance data delivery rate in MWSN.

$FREE$ —Fine-Grained Scheduling for Reliable and Energy-Efficient Data Collection in LoRaWAN

LoRaWAN promises to provide wide-area network access to low-cost devices that can operate for up to ten years on a single 1000-mAh battery. This makes LoRaWAN particularly suited for the data collection applications (e.g., monitoring applications), where device lifetime is a key performance metric. However, when supporting a large number of devices, LoRaWAN suffers from a scalability issue due to the high collision probability of its Aloha-based MAC layer. The performance worsens further when using acknowledged transmissions due to the duty-cycle restriction at the gateway. For this, we propose FREE , a fine-grained scheduling scheme for reliable and energy-efficient data collection in LoRaWAN. FREE takes advantage of applications that do not have hard delay requirements on data delivery by supporting the synchronized bulk data transmission. This means data are buffered for transmission in scheduled time slots instead of transmitted straight away. FREE allocates spreading factors, transmission powers, frequency channels, time slots, and schedules slots in frames for LoRaWAN end-devices. As a result, FREE overcomes the scalability problem of LoRaWAN by eliminating collisions and grouping acknowledgments. We evaluate the performance of FREE versus different legacy LoRaWAN configurations. The numerical results show that FREE scales well and achieves almost 100% data delivery and the device lifetime is estimated over ten years independent of traffic type and network size. In comparison to poor scalability, low data delivery and device lifetime of fewer than two years for acknowledged data traffic in the standard LoRaWAN configurations.

Topology Management and TSCH Scheduling for Low-Latency Convergecast in In-Vehicle WSNs

Wireless sensor networks (WSNs) are considered as a promising solution in intravehicle networking to reduce wiring and production costs. This application requires reliable and real-time data delivery, while the network is very dense. The time-slotted channel hopping (TSCH) mode of the IEEE 802.15.4 standard provides a reliable solution for low-power networks through guaranteed medium access and channel diversity. However, satisfying the stringent requirements of in-vehicle networks is challenging and demands for special consideration in network formation and TSCH scheduling. This paper targets convergecast in dense in-vehicle WSNs, in which all nodes can potentially directly reach the sink node. A cross-layer low-latency topology management and TSCH scheduling (LLTT) technique is proposed that provides a very high timeslot utilization for the TSCH schedule and minimizes communication latency. It first picks a topology for the network that increases the potential of parallel TSCH communications. Then, by using an optimized graph isomorphism algorithm, it extracts a proper match in the physical connectivity graph of the network for the selected topology. This network topology is used by a lightweight TSCH schedule generator to provide low data delivery latency. Two techniques, namely grouped retransmission and periodic aggregation, are exploited to increase the performance of the TSCH communications. The experimental results show that LLTT reduces the end-to-end communication latency compared to other approaches, while keeping the communications reliable by using dedicated links and grouped retransmissions.

LEEF: Latency and energy efficient federation of disjoint wireless sensor segments

In hostile environments where explosives and natural calamities probably occur, wireless sensor networks (WSNs) are susceptible to multiple collocated failures and could be partitioned into disjoint segments. Federating the segments would be essential for restoring connectivity and enabling data sharing in the network. The federation may be achieved by populating relay nodes and providing perpetual inter-segment paths. In this paper, we tackle the federation problem while considering constrained relay availability, i.e., a limited number (k) of mobile relays to provide intermittent inter-segment connectivity that makes the problem more challenging. We propose LEEF, a novel algorithm for achieving energy-efficient federation with low inter-segment data delivery latency. LEEF strives to group the segments into k clusters in a star topology where a cluster at the center of the area serves as a hub between each pair of segment-clusters. Each cluster is served by a distinct mobile relay. In addition, LEEF opts to equalize the energy consumed by the k mobile relays due to travel and wireless communication. We analyze the properties of LEEF mathematically and validate its performance through extensive simulation experiments.

5G Optimized Caching and Downlink Resource Sharing for Smart Cities

In smart cities, millions of things, systems, and people are interconnected and communicate with each other over wireless sensor networks, Internet of Things (IoT), and 5G networks. A tremendous amount of data traffic, which is frequently generated by the things in wireless multimedia sensor networks (WMSNs) and/or IoT, is accessed by a massive number of mobile users (MUs). These MUs are all competing to access the 5G network for data as well as urban applications and services. This can in turn cause exhaustion to the 5G network. In such cases, users can experience low data delivery and traffic congestions through backhaul links by macro base stations (MBSs). In this paper, we propose a joint caching and downlink resource sharing optimization framework (CSF) in 5G networks to assist WMSNs to efficiently deliver multimedia contents to the MUs. The CSF enables the MBSs to optimally decide how many replicas of each multimedia content to cache in which fem to base stations for high multimedia content hit rate. It also optimally exploits the MUs that are willing to share their downlink resources and that have retrieved multimedia contents, for offloading with device-to-device communications. The objective is to eventually maximize the system delivery capacity. Simulation results demonstrate that the CSF provides the best performance in terms of hit rate and system delivery capacity.

Adaptive scheme to Control Power Aware for PDR in Wireless Sensor Networks

Nowadays Wireless sensor networks playing vital role in all area. Which is used to sense the environmental monitoring, Temperature, Soil erosin etc. Low data delivery efficiency and high energy consumption are the inherent problems in Wireless Sensor Networks. Finding accurate data is more difficult and also it will leads to more expensive to collect all sensor readings. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data, provide opportunities for reducing the energy consumption of continuous sensor data collection and to achieve network energy efficiency and stability. So as we propose Dynamic scheme for energy consumption and data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme, sleep/awake method with dynamic scheme. Our framework is clustering based. A cluster head represents all sensor nodes within the region and collects data values from them. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical dynamic algorithm for data aggregation, it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme with dynamic scheme for clustering-based continuous data collection in sensor networks. When a cluster node fails because of energy depletion we need to choose alternative cluster head for that particular region. It will help to achieve less energy consumption. Our proposed models, analysis, and framework are validated via simulation and comparison with Static Cluster method in order to achieve better energy efficiency and PDR.

Influence of availability of TAO data on NCEP ocean data assimilation systems along the equatorial Pacific

AbstractIt is reasonable to assume that the quality of an ocean reanalysis will depend not only on the ocean data assimilation system but also on the availability of observational data. Among the data assimilated in the tropical Pacific, observations from the Tropical Atmosphere Ocean (TAO) moored array are an important contributor. Due to various reasons, in the last couple of years, TAO had a very low data delivery rate at some of the mooring sites. In this work, we examined the influence of the availability of in situ TAO data from all 10 equatorial mooring sites in 1999–2014 by comparing ocean temperature from two data assimilation systems at National Centers for Environmental Prediction (NCEP): the Global Ocean Data Assimilation System (GODAS) and the Climate Forecast System Reanalysis (CFSR). Our working hypothesis is that missing of in situ observational data should lead to less observational constraint, and consequently, larger divergence between ocean reanalyses. Compared with periods of no in situ TAO observations, the disagreements of ocean temperature between CFSR and GODAS are indeed smaller when in situ TAO observations are available. The disagreements are largest along the thermocline due to its being the region of strongest variability. Thus, it is suggested that without the constraint of in situ TAO observations, the inconsistency between the ocean reanalyses from two NCEP data assimilation systems is larger, highlighting the contribution of TAO observations. Moreover, it is also suggested that the disagreement of ocean temperature between GODAS and CFSR is mainly caused by the difference of the models used in the assimilation systems and the impact of in situ TAO availability plays a secondary, but important role. To improve the quality of the assimilation products, it is necessary both to reduce model bias and to assimilate more high‐quality data.

An adaptive scheme for data collection and aggregation in periodic sensor networks

Low data delivery efficiency and high energy consumption are inherent problems in sensor networks. Finding accurate data is difficult and leads to expensive sensor readings. Adaptive data collection and aggregation techniques provide opportunities for reducing the energy consumption of continuous sensor data collection. In this paper, we propose an adaptive data collection and aggregation scheme for periodic sensor networks. Our approach is two-fold: firstly, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, over-sampling can be minimised and power efficiency can be improved. Furthermore, we propose a multiple levels activity model that uses behaviour functions modelled by modified Bezier curves to define application classes. Secondly, a new data aggregation method is applied on the set of data collected from all sensor nodes. We investigate the problem of finding all pairs of nodes generating similar datasets. Our proposed models are validated via simulation on real sensor data.

Rendezvous-based data dissemination for supporting mobile sinks in multi-hop clustered wireless sensor networks

In wireless sensor networks, a clustering-based technique is considered as an efficient approach for supporting mobile sinks without using position information. It exploits a Backbone-based Virtual Infrastructure (BVI) which uses only cluster heads (CHs) to construct routing structures. Since sensor nodes have constrained energy and are failure-prone, the effective design of both a clustering structure to construct a BVI and a routing protocol in the BVI is an important issue to achieve energy-efficient and reliable data delivery. However, since previous studies use one-hop clustering for a BVI, they are not robust against node and link failures and thus leading low data delivery ratio. They also use flooding-based routing protocols in a BVI and thus leading high energy consumption. Thus, in this paper, we propose a rendezvous-based data dissemination protocol based on multi-hop clustering (RDDM). Since RDDM uses a multi-hop clustering to provide enough backup sensor nodes to substitute a CH and enough backup paths between neighbor CHs, it can provide high robustness against node and link failures. By using a rendezvous CH, RDDM constructs routing paths from source nodes to mobile sinks without flooding in our BVI and thus can save energy of sensor nodes. By considering movement types of sinks, RDDM finds out a shorter path between a source node and a mobile sink through signaling only between neighbor CHs and thus can reduce the energy consumption. Analysis and simulation results show that RDDM provides better performance than previous protocols in terms of energy consumption and data delivery ratio.

Virtual Line-Based Data Dissemination for Mobile Sink Groups in Wireless Sensor Networks

Mobile sink groups such as rescue teams or platoons bring about a new challenging issue for handling mobility in wireless sensor networks. Recently, a flooding-based technique has been proposed to disseminate data to a mobile sinks group. However, since it simply use flooding of data within the sink group region, it leads to high energy consumption of sensor nodes and low data delivery ratio to sinks due to the broadcast storm problem. Thus, this letter proposes a protocol called Virtual Line-based Data Dissemination (VLDD) to achieve energy-efficient and reliable data dissemination. Instead of flooding, VLDD exploits a Virtual Line Structure (VLS) for data storage within the group region. While sources store their own data on the VLS, each sink retrieves data from the VLS at its location in a time of need. Analysis and simulation results show that VLDD achieves better performance than the existing protocol.

Taming Uncertainties in Real-Time Routing for Wireless Networked Sensing and Control

Real-time routing is a basic element of closed-loop, real-time sensing and control, but it is challenging due to dynamic, uncertain link/path delays. The probabilistic nature of link/path delays makes the basic problem of computing the probabilistic distribution of path delays NP-hard, yet quantifying probabilistic path delays is a basic element of real-time routing and may well have to be executed by resource-constrained devices in a distributed manner; the highly varying nature of link/path delays makes it necessary to adapt to in-situ delay conditions in real-time routing, but it has been observed that delay-based routing can lead to instability, estimation error, and low data delivery performance in general. To address these challenges, we propose the Multi-Timescale Estimation (MTE) method; by accurately estimating the mean and variance of per-packet transmission time and by adapting to fast-varying queueing in an accurate, agile manner, MTE enables accurate, agile, and efficient estimation of probabilistic path delay bounds in a distributed manner. Based on MTE, we propose the Multi-Timescale Adaptation (MTA) routing protocol; MTA integrates the stability of an ETX-based directed-acyclic-graph (DAG) with the agility of spatiotemporal data flow control within the DAG to ensure real-time data delivery in the presence of dynamics and uncertainties. We also address the challenges of implementing MTE and MTA in resource-constrained devices such as TelosB motes. We evaluate the performance of MTA using the NetEye and Indriya sensor network testbeds. We find that MTA significantly outperforms existing protocols, e.g., improving deadline success ratio by 89% and reducing transmission cost by a factor of 9.7 in the NetEye testbed.

A Multipopulation Firefly Algorithm for Correlated Data Routing in Underwater Wireless Sensor Networks

Low data delivery efficiency and high energy consumption are the inherent problems in Underwater Wireless Sensor Networks (UWSNs) characterized by the acoustic channels. Existing energy-efficient routing algorithms have been shown to reduce energy consumption of UWSNs to some extent, but still neglect the correlation existing in the local data of sensor nodes. In this paper, we present a Multi-population Firefly Algorithm (MFA) for correlated data routing in UWSNs. We design three kinds of fireflies and their coordination rules in order to improve the adaptability of building, selecting, and optimization of routing path considering the data correlation and their sampling rate in various sensor nodes. Different groups of fireflies conduct their optimization in the evolution in order to improve the convergence speed and solution precision of the algorithm. Moreover, after the data packets are merged during the process of routing path finding, MFA can also eliminate redundant information before they are sent to the sink node, which in turn saves energy and bandwidth. Simulation results have shown that MFA achieves better performance than existing protocols in metrics of packet delivery ratio, energy consumption, and network throughput.