Data Delivery Path Research Articles

Service Continuity Based Data Delivery Optimization in Satellite-Terrestrial Networks

The satellite-terrestrial networks (STNs) enable global data delivery with low latency and high transmission rate utilizing the Low-Earth-Orbit (LEO) satellite constellation, especially for users in remote regions. However, the dynamic nature of STN presents two challenges to the quality of experience (QoE): 1) the dynamic topology in different time slots; 2) the dynamic path planning for each user. These raise unique problems for the optimization of data delivery paths in STN with service continuity guarantee. In this paper, we first model the STN by a time-varying graph to describe the data delivery in a certain service duration. An indicator called user satisfaction is constructed to measure delivery performance. Aiming at maximizing total user satisfaction, we reformulate the STN path planning problem by utilizing the spatial-temporal decomposition. Considering the path planning in each time slot, we modify the heuristic algorithm to optimize the allocation of transmission resources for different users. In the temporal dimension, we show that the path allocation results in previous time slots directly affect subsequent path calculation then propose an active time slot division algorithm to improve the STN service continuity. Excellent STN transmission performance is matched to the best fit time slot duration to obtain efficient and continuous data delivery. Experimental results show that the proposed STN data delivery optimization scheme achieves superior transmission performance with better service continuity.

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.

EEGT: Energy Efficient Grid-Based Routing Protocol in Wireless Sensor Networks for IoT Applications

The Internet of Things (IoT) integrates different advanced technologies in which a wireless sensor network (WSN) with many smart micro-sensor nodes is an important portion of building various IoT applications such as smart agriculture systems, smart healthcare systems, smart home or monitoring environments, etc. However, the limited energy resources of sensors and the harsh properties of the WSN deployment environment make routing a challenging task. To defeat this routing quandary, an energy-efficient routing protocol based on grid cells (EEGT) is proposed in this study to improve the lifespan of WSN-based IoT applications. In EEGT, the whole network region is separated into virtual grid cells (clusters) at which the number of sensor nodes is balanced among cells. Then, a cluster head node (CHN) is chosen according to the residual energy and the distance between the sink and nodes in each cell. Moreover, to determine the paths for data delivery inside the cell with small energy utilization, the Kruskal algorithm is applied to connect nodes in each cell and their CHN into a minimum spanning tree (MST). Further, the ant colony algorithm is also used to find the paths of transmitting data packets from CHNs to the sink (outside cell) to reduce energy utilization. The simulation results show that the performance of EEGT is better than the three existing protocols, which are LEACH-C (low energy adaptive clustering hierarchy), PEGASIS (power-efficient gathering in sensor information systems), and PEGCP (maximizing WSN life using power-efficient grid-chain routing protocol) in terms of improved energy efficiency and extended the lifespan of the network.

Healthcare Internet of Things (HIoT) data security enhancement using blockchain technology

In recent years, the Internet of Things (IoT) has attracted more attention after the integration of IoT devices with the cloud for data management. IoT is used for sharing data in healthcare services. However, security and privacy vulnerabilities still exist in data transfers to and from cloud environments. Due to memory limitations, it is difficult to implement security protocols in IoT devices. So an intermediate secure node is established to enhance data security and privacy while handling healthcare data. Blockchain is a renowned technology that is adopted in various data management and security applications, but its potential is not unleashed effectively in healthcare data management. Therefore in this research work, firstly, the data is pre-processed and then the Ant colony optimization method is used to find the shortest path for efficient data delivery. And, a blockchain based data security approach for healthcare data management is presented. The main objective is to enhance security against healthcare data threats. In the IoTs fog layer, a public-permissioned blockchain security process with elliptical curve cryptography and digital signature is presented as a distributed ledger database that provides immutable security, transparency in transactions, and prevents data tampering. The performance of the proposed approach is verified through various parameters like certificate generation time and size, data retrieval time, and size for better validation.

An Efficient Multipath Routing Protocol for Decentralized Wireless Sensor Networks for Mission and Safety-Critical Systems

Background: Wireless Sensor Network (WSN) is a useful integral part of mission and safety-critical systems, whose failure may result in injury, loss of life, serious environmental damage and/or may result in the failure of some goal-directed activities. Its major challenges are reliability, and security related issues during data delivery. To mitigate these, the development of secure routing protocols, which are capable of optimum utilization of WSNs’ nodes resources for effective data delivery, has been one of the major research interests. However, most of the existing single path routing protocols for WSNs are not able to meet with up with the performance requirements of the mission and safety-critical systems. Meanwhile, a few that are multipath require high computational power, energy and their multiple paths are not ranked, this makes them unsuitable as routing protocol for WSNs in mission and safety-critical systems. Objective: In this work, we propose a secure multipath routing protocol based on sectorisation and best neighbouring nodes selection models to meet up with the performance requirements of WSNs in the mission and safety-critical systems. The protocol is capable of providing optimal multiple ranked route paths for reliable data delivery. Methods: A route management technique, using direct approach, is developed for selecting different optimal data paths for reliable data routing. Also, simple but efficient lightweight privacy preserving authentication scheme is proposed for the protocol to ensure security and privacy during data routing. Computational and security analysis were performed to ascertain its efficiency in terms of computational cost, energy consumption and security. Results: The results showed that SMRP achieved better performance compared to the two stateof- the-art solutions in terms of end-to-end delay, energy consumption and data routing reliability. Conclusion: The proposed protocol is suitable for wireless sensor network due to its low delay, low energy consumption and routing reliability metrics.

BRT: Bus-Based Routing Technique in Urban Vehicular Networks

Routing data in Vehicular Ad hoc Networks is still a challenging topic. The unpredictable mobility of nodes renders routing of data packets over optimal paths not always possible. Therefore, there is a need to enhance the routing service. Bus Rapid Transit systems, consisting of buses characterized by a regular mobility pattern, can be a good candidate for building a backbone to tackle the problem of uncontrolled mobility of nodes and to select appropriate routing paths for data delivery. For this purpose, we propose a new routing scheme called Bus-based Routing Technique (BRT) which exploits the periodic and predictable movement of buses to learn the required time (the temporal distance) for each data transmission to Road-Side-Units (RSUs) through a dedicated bus-based backbone. Indeed, BRT comprises two phases: (i) Learning process which should be carried out, basically, one time to allow buses to build routing tables entries and expect the delay for routing data packets over buses, (ii) Data delivery process which exploits the pre-learned temporal distances to route data packets through the bus backbone towards an RSU (backbone mode). BRT uses other types of vehicles to boost the routing of data packets and also provides a maintenance procedure to deal with unexpected situations like a missing nexthop bus, which allows BRT to continue routing data packets. Simulation results show that BRT provides good performance results in terms of delivery ratio and end-to-end delay.

EHCP: An Efficient Hybrid Content Placement Strategy in Named Data Network Caching

The abilities of in-network caching in Named Data Networking (NDN) agrees to enable effective data dissemination throughout the globe without requiring the end-to-end communication infrastructure. The goal of this study is to develop an efficient content placement strategy for the NDN caching module to achieve the enhanced performance for the NDN network. In this work, optimizations of the problems in NDN-based caching strategies are formulated and to solve these problems, a new caching strategy is designed Named as Efficient Hybrid Content Placement (EHCP). The aim of the EHPC strategy is to reduce the multiple replications of homogeneous contents at numerous locations during data dissemination and to increase the diverse number of contents along the data delivery path. In addition, the effect of the cache-hit ratio, hop-decrement, and content redundancy is shown by comparing the proposed caching strategy with other state-of-the-art caching strategies in a simulation environment. The consequences show that the proposed strategy delivers an enhanced number of heterogeneous contents in terms of improving content diversity ratio along with the higher cache-hit ratio. Moreover, with different cache sizes, EHCP performs better in terms of content redundancy and hop-decrement as compared to the existing state-of-the-art strategies.

Improving reporting delay and lifetime of a WSN using controlled mobile sinks

Wireless sensor networks (WSNs) are characterized by many to one traffic pattern, where a large number of nodes communicate their sensed data to the sink node. Due to heavy data traffic near the sink node, the nodes closer to sink node tends to exhaust their energy faster compared to those nodes which are situated away from the sink. This may lead to the fragment of a network due to the early demise of sensor nodes situated closer to the sink. To pacify this problem, mobile sinks are proposed for WSNs. Mobile sinks are capable to provide uniform energy consumption, load distribution, low reporting delay and quick data delivery paths. However, the position of the mobile sink needs to be updated regularly as such position update messages may reduce the network lifetime. In this paper, we propose a novel Location Aware Routing for Controlled Mobile Sinks (LARCMS), which will help in minimizing reporting delay, enhancing network lifetime, handling sink position updates and providing uniform energy consumption. The proposed technique uses two mobile sinks in predefined trajectory for data collection and provides better results compared to existing techniques. The performance of LARCMS is evaluated by comparing with similar mobile sink routing protocols through extensive simulations in MATLAB.

Performance evaluation of secure multipath retransmission mechanism in next generation heterogeneous communication systems

With the standardisation of the fifth generation (5G) wireless network interface was established in 3GPP, the features of 5G arouse great interest both in academics and industry, even government. The significant advancements of 5G wireless network technology will make a great increase in the demand for content-rich streaming media services. What's more, many mobile devices with heterogeneous network interfaces (4G, WiFi, and even 5G) connect multiple paths for data delivery simultaneously. Multiple Transmission Control Protocol (MPTCP) is recommended by Internet Engineering Task Force (IETF) to multi-path parallel transmission, which is an important transport layer protocol. Although there are many attractive benefits by using MPTCP for data delivery, it becomes sensitive for MPTCP to network attacks. When a subflow with the MPTCP association suffers from some network attacks (e.g. denial of service attacks), there is undoubted that it will cause large numbers of retransmission data. Unfortunately, the regular MPTCP retransmission mechanisms retransmit data over the same path, which may cause receiver buffer blocking. In this study, the authors propose effective retransmission mechanisms in MPTCP for retransmitting data on a non-congested path. The simulation results show that the proposed retransmission mechanism has better performance than the regular’ in the network attacks.

Heuristic‐based link quality preservation for reliable data delivery

SummaryDuring the link failure conditions in Voice over Internet Protocol applications, the backup of routing information is an important requirement to offer the reliable data delivery to the destination. But the maintenance of multiple paths is the difficult and time consuming for the dynamic environment. But the hops reduction between the source and destination and the interference occurrence causes the transmission in multiple times and that leads to minimum network lifetime. Hence, the preservation of link between the multiple hops is the necessary task to improve the performance. This paper validates the link quality with the 4 measures as received signal strength indicator, bandwidth, node mobility, and the number of connections. The link quality factor (LQF) measurement considers the signal and noise level in 5 operating conditions to allow the automatic identification of closest neighbors and their location. The integration of tabu list and the heuristic‐based fitness search algorithm constructs the routing path and updates the path to identify the link failure. The LQF estimation and the dynamic update provide an efficient data delivery path with less number of iterations. The comparative analysis between the proposed hybrid tabu‐heuristic–based LQF with the existing protocols such as Adhoc On‐Demand Vector (AODV), Optimized Link State Routing (OLSR), and the Interference‐based Topology Control algorithm for Delay‐constrained (ITCD) proves the effectiveness of tabu‐heuristic–based LQF regarding the throughput, energy, packet delivery ratio with the minimum delay, and rebroadcasting probabilities.

Energy Balance Movement Schemes for Maximizing Network Lifetime in WSRNs

Wireless sensor and robot network (WSRN) is composed of a set of sensors and robots. The mobility of robots has been proved to be quite useful in prolonging the network lifetime. However, mobility is the major energy consumption of robot. Thus, the mobility of robots requires intelligent control in order to efficiently complete appointed tasks. Compared with lightweight sensors, using robots to transmit heavy data (such as audio or video data) is more appropriate. Therefore, a communication path, which is composed of robots between the base station (BS) and the event point, should be maintained. In this paper, three movement schemes called greedy back-up (GB), stretched back-up path (SBP) and back-up path regression (BPR) approaches, are proposed, aiming to construct the data delivery path while maximising the network lifetime. Experimental results show that the proposed schemes outperform the compared MH-MDRM-CP approach in terms of network lifetime and moving distance.

FDR: fault detection and recovery scheme for wireless sensor networks using virtual grid

Due to autonomous operation and constrained resources, nodes of a wireless sensor network are susceptible to failures. Due to multiple node failures, network topology may change or partition into many disconnected segments causing data/query path breakage. Early detection and recovery of faults is desirable in most scenarios. Also, due to limited battery life of a node, the solution must consume minimum possible energy for prolonged network operation. Hence in this paper, we propose a Fault Detection and Recovery scheme which is an energy efficient fault detection and recovery strategy that achieves minimum data loss by efficiently replacing faulty node on the present route or by finding full or partial alternate paths. Topology is managed by constructing a virtual grid over the entire network which helps in managing dynamically changing network topology easily and makes failure detection and recovery effective. It also helps to create energy efficient path between a source and a sink by finding shortest possible path. Further, nodes’ cooperation is exploited to create certain zones on the data/query path which provides alternate nodes or possible alternate paths if required on some node failures. Thus, scheme achieves fault tolerance and at the same time achieves energy efficiency by always selecting shortest path for data delivery between source and sink. Analytical and simulation study reveals the significant improvement in terms of fault detection and energy conservation over existing similar schemes.

Learning Data Delivery Paths in QoI-Aware Information-Centric Sensor Networks

In this paper, we envision future sensor networks to be operating as information-gathering networks in large-scale Internet-of-Things applications such as smart cities, which serve multiple users with diverse quality-of-information (QoI) requirements on the data delivered by the network. To learn data delivery paths that dynamically adapt to changing user requirements in this information-centric sensor network (ICSN) environment, we make use of cognitive nodes that implement both learning and reasoning in the network. In this paper, we focus on the learning strategies and propose two techniques, namely learning data delivery A* (LDDA*) and cumulative-heuristic accelerated learning (CHAL) that use heuristics to improve the success rate of data delivered to the sink in the cognitive ICSN. While LDDA* updates a single heuristic function to choose paths that can deliver data with good QoI to the sink, CHAL accumulates heuristic values from multiple observations from the environment to choose data delivery paths that are more resource aware and considerate toward the energy consumption of the network. Extensive simulations have shown improvement of about 40% in the average rate of successful data delivery to the sink with the use of heuristic learning, when compared with a network that did not implement any learning.

Information-centric sensor networks for cognitive IoT: an overview

Information-centric sensor networks (ICSNs) are a paradigm of wireless sensor networks that focus on delivering information from the network based on user requirements, rather than serving as a point-to-point data communication network. Introducing learning in such networks can help to dynamically identify good data delivery paths by correlating past actions and results, make intelligent adaptations to improve the network lifetime, and also improve the quality of information delivered by the network to the user. However, there are several factors and limitations that must be considered while choosing a learning strategy. In this paper, we identify some of these factors and explore various learning techniques that have been applied to sensor networks and other applications with similar requirements in the past. We provide our recommendation on the learning strategy based on how well it complements the needs of ICSNs, while keeping in mind the cost, computation, and operational overhead limitations.

Cluster Based Energy Efficient Routing Protocol Using ANT Colony Optimization and Breadth First Search

This paper presents an algorithm to choose optimal path for data delivery for continuous monitoring of vital signs of patients in Body Area Network (BAN) in hospital indoor environments where a large number of patients exist and the traffic generated on the path rapidly changes over time. The methodology for finding the optimal path includes a meta-heuristic that combines ANT Colony Optimization (ACO) using Clustering. We propose a ACOBAN Clustering for monitoring BAN data and propose a method to improve network life, energy, load balancing on the overall network. Since traffic generated by BANs on the network changes with time, so the optimal path is important in Wireless Body Area Network. Our algorithm ensures network connectivity by using a mechanism of modified Cluster Head rotation process level by level and using breadth first search algorithm which avoids trapping during exploration. In the current work, we implemented ACO method and done experimental results on OMNeT++ to prove that the proposed method can find a better solution than conventional methods.

Data collection for multiple mobile users in wireless sensor networks

Data collection is one of the fundamental functions in wireless sensor networks (WSNs). Different from a typical WSN with only a single and stationary sink, this paper considers a scenario with multiple mobile sinks. The motivation is for a WSN to support future applications, such as Internet of Things (IoT). In this case, a WSN requires abilities to deliver sensing results to multiple users roaming in the network. There are two difficulties in this problem: sink mobility and multiple sinks. Since sinks are mobile, data delivery paths need to be updated frequently, which causes huge maintenance cost. To resolve this problem, we propose a hop-count-based architecture together with an efficient mobility management scheme. On the other side, sensing results from a large number of sensors are delivered to multiple sinks, which causes lots of packet transmissions. To reduce transmission cost, the concepts of multicast and data aggregation are integrated and applied. We first prove that a distributed multicast decision is a NP-hard problem and then propose a heuristic solution. Afterward, we further merge data aggregation into multicast and propose a distributed many-to-many aggregation scheme. Simulations are constructed to evaluate the efficiency of the proposed schemes. The results show that both the proposed multicast scheme and many-to-many aggregation scheme can efficiently reduce transmission cost and energy when delivering data to multiple mobile sinks.

Multipath routing in mobile ad hoc network with probabilistic splitting of traffic

Multipath routing is a burning issue in mobile ad hoc network due to its various advantages over single path routing. Some of these advantages are load balancing, bandwidth aggregation, and fault tolerance. Multipath routing means multiple paths exist between source and destination pair. Many works discussed in section 2 addressed queuing delays, but none of them suggested queuing delay for multiple path deliveries of data in mobile ad hoc network context. In this paper, we have designed a mathematical model to compute delay and throughput for multipath. Our model follow the network of M/M/1 queues, and we have applied Burke's theorem to calculate the queuing delay of the packet in mobile network scenario. This model can be used to estimate delay and throughput of an individual path. Further, through the analysis the best path for data delivery out of available multiple paths as well as the multipath path can be used simultaneously for data delivery to the destination. Simulation result shows that splitted traffic multiple paths outperform splitted traffic. Therefore, our model is useful for design and analysis of ad hoc network. The simulation work has been carried out in Qualnet simulator.

A data delivery framework for cognitive information-centric sensor networks in smart outdoor monitoring

Cognitive information-centric sensor networks represent a paradigm of wireless sensor networks in which sensory information is identified from the network using named-data, and elements of cognition are used to deliver information to the sink with quality that satisfies the end-user requirements. Specialized nodes called Local Cognitive Nodes (LCNs) implement knowledge representation, reasoning and learning as elements of cognition in the network. These LCNs identify user-requested sensory information, and establish data delivery paths to the sink by prioritizing Quality of Information (QoI) attributes (e.g., latency, reliability, and throughput) at each hop based on the network traffic type. Analytic Hierarchy Processing (AHP) is the reasoning tool used to identify these paths based on QoI-attribute priorities set by the user. From extensive simulations, parameters that can be controlled to improve the values of QoI attributes along each hop were identified, and performance of the AHP-based data-delivery technique was compared with two traditional data-centric techniques in terms of lifetime and QoI attribute performance. It was found that the use of cognition improves the number of successful transmissions to the sink by almost 30%, while closely adapting the data delivery paths to the QoI requirements of the user.

Efficient multicast tree construction in wireless mesh networks

Multicast routing algorithms designed for wireline networks are not suitable for wireless environments since they cannot efficiently exploit the inherent characteristics of wireless networks such as the broadcast advantage. There are many routing protocols trying to use these advantages to decrease the number of required transmissions or increase the reception probability of data (e.g., opportunisticrouting). Reducing the number of transmissions in a multicast tree directly decreases the bandwidth consumption and interference and increases the overall throughput of the network. In this paper, we introduce a distributed multicast routing protocol for wireless mesh networks called NCast which take into account the data delivery delay and path length when constructing the tree. Furthermore, it effectively uses wireless broadcast advantage to decrease the number of forwarding nodes dynamically when a new receiver joins the tree. Our simulation results show that NCast improves network throughput, data delivery ratio and data delivery delay in comparison with on demand multicast routing protocol. It is also comparable with multichannel multicast even though it does not use channeling technique which eliminates the interference inherently.

CMT‐CQA: Cross‐layer QoS‐aware adaptive concurrent multipath data transfer in heterogeneous networks

Stream control transport protocol (SCTP)‐based concurrent multipath transfer (CMT) can help multi‐homed devices to increase their throughput by making use of parallel transmissions over multiple paths and bandwidth aggregation. However, if CMT cannot identify wireless error, it cannot really achieve the desired performance. Furthermore, if CMT only utilizes all available paths for data delivery, it will undoubtedly degrade application‐level performance since the asymmetric paths may involve large quality differences. This paper proposes a novel cross‐layer quality‐of‐service (QoS)‐aware adaptive CMT (CMT‐CQA) with the following aims: (i) to provide an adaptive ‘CMT‐to‐partial CMT’ adjustment strategy for efficient bandwidth aggregation by jointly considering transport layer QoS, MAC layer QoS, and path history information; (ii) to address an enhanced congestion window (cwnd) fast recovery mechanism to reduce bursty transmission in multi‐homed wireless network environments where fail‐over occurs frequently; and (iii) to introduce a proper multimedia transmission behavior to improve users' quality of experience (QoE) for multimedia streaming service. Simulation results show that CMT‐CQA outperforms the existing CMT solutions in terms of performance and QoS. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.