E2E Delay Research Articles

Energy efficient routing in IOT based UWSN using bald eagle search algorithm

AbstractThe underwater wireless sensor network (UWSN) faces many problems such as limited bandwidth, large path loss, high attenuation, and limited battery power. In recent times, different routing protocols have been proposed in the Internet of Things (IoT) enabled UWSN (IoT‐UWSNs) to explore the underwater environment for different purposes, that is, scientific and military purposes. However, high energy consumption (EC), end to end (E2E) delay, low packet delivery ratio (PDR) and minimum network lifetime make the energy efficient communication a challenging task in UWSN. The high E2E delay, EC, and reliable data delivery are the critical issues, which play an important role to enhance the network throughput. For effective achievement of EC, proposed a new routing algorithm called bald eagle search (BES). It is a nature‐inspired optimization algorithm that copied bald eagle hunting behavior. The proposed routing protocol includes three stages: initialization stage, construction stage, and the data transmission phase. The nodes identify the location and the sector during the initialization process. The construction phase includes two steps, neighbor node selection, and identification of energy efficient path. Then the last phase includes the transmission, in this the broadcasting node chooses the nodes with high residual energy (RE) in comparison to the average RE of the entire network. The proposed method is implemented in MATLAB. The outcomes of simulation confirm that the BES algorithm has better performance on EC, average RE, and network lifetime than the three existing algorithm in UWSN.

Optimizing Energy Consumption in IoT-Based Scalable Wireless Sensor Networks

In recent era of IoT, energy ingesting by sensor nodes in Wireless Sensor Networks (WSN) is one of the key challenges. It is decisive to diminish energy ingesting due to restricted battery lifespan of sensor nodes, Objective of this research is to develop efficient routing protocol/algorithm in IoT based scenario to enhance network performance with QoS parameters. Therefore, keeping this objective in mind, a QoS based Optimized Energy Clustering Routing (QOECR) protocol for IoT based WSN is proposed and evaluated. QOECR discovers optimal path for sink node and provides better selection for sub-sink nodes. Simulation has been done in MATLAB to assess the performance of QOECR with pre-existing routing protocols. Simulation outcomes represent that QOECR reduces E2E delay 30%-35%, enhances throughput 25%-30%, minimizes energy consumption 35%-40%, minimizes packet loss 28%-32%, improves PDR and prolongs network lifetime 32%-38% than CBCCP, HCSM and ZEAL routing protocols.

Energy Efficient Proactive Routing Scheme for Enabling Reliable Communication in Underwater Internet of Things

The Underwater Internet of Things (UIoT) is a network of smart interconnected devices operating under various aqueous environments. In UIoT, due to aqueous feature of signal absorption, data signals are transmitted at low frequency. Similarly, significant interference and collisions degrade transmission quality, resulting in a low Packet Delivery Ratio (PDR) and a long End-to-End (E2E) delay. Moreover, a significant amount of energy is being wasted due to the void hole problem, which arises when the source node does not identify an instant forwarder node. Thus, a reliable communication with enhanced network lifetime is highly desired in UIoT. Therefore, this work proposes a proactive energy Efficient Layer-by-Layer Watchman-based Collision Free Routing (ELW-CFR) scheme to address the above mentioned issues. The proposed scheme provides the low E2E delay and high PDR while avoiding the void hole problem. Extensive simulations have been performed to show the performance superiority of the proposed scheme against the state-of-the-art schemes.

Optimizing Energy Consumption in IoT based Scalable Wireless Sensor Networks

In recent era of IoT, energy ingesting by sensor nodes in Wireless Sensor Networks (WSN) is one of the key challenges. It is decisive to diminish energy ingesting due to restricted battery lifespan of sensor nodes, Objective of this research is to develop efficient routing protocol/algorithm in IoT based scenario to enhance network performance with QoS parameters. Therefore, keeping this objective in mind, a QoS based Optimized Energy Clustering Routing (QOECR) protocol for IoT based WSN is proposed and evaluated. QOECR discovers optimal path for sink node and provides better selection for sub-sink nodes. Simulation has been done in MATLAB to assess the performance of QOECR with pre-existing routing protocols. Simulation outcomes represent that QOECR reduces E2E delay 30%-35%, enhances throughput 25%-30%, minimizes energy consumption 35%-40%, minimizes packet loss 28%-32%, improves PDR and prolongs network lifetime 32%-38% than CBCCP, HCSM and ZEAL routing protocols.

Deep Q-Network and Traffic Prediction based Routing Optimization in Software Defined Networks

Software Defined Networking (SDN) is gaining momentum not only in research but also in IT industry representing the drivers of 5G networks, due to its capabilities of increasing the flexibility of a network and address a variety of network challenges, by logically centralizing the intelligence in software-based controllers. Thanks to Machine Learning (ML) techniques, the network performances and utilization can be optimized and enhanced. Neural Networks (NN) and Reinforcement Learning (RL), in particular, have demonstrated great success in cooperating with complex problems arising in network operation and management. To this end, we exploit in this paper, an SDN-based rules placement approach that aims to dynamically predict the traffic congestion by using mainly NN and learn optimal paths and reroute traffic to improve network utilization by deploying a Deep Q-Network (DQN) agent. To this end, we first formulate the Quality-of-Service (QoS)-aware routing problem as a Linear Program (LP), whose objective is to minimize the end-to-end (E2E) delay and link utilization. Then, we propose a simple yet efficient heuristic algorithm to solve it. Numerical results through emulation using ONOS controller and Mininet demonstrate that the proposed approach can significantly improve network performances in terms of decreasing the link utilization, the packet loss and the E2E delay.

Seamless Health Monitoring Using 5G NR for Internet of Medical Things.

Nowadays, information and communication technology grows rapidly. The microelectronics and communication mediums also enhance their reachability of coverage and connectivity. 5G enhances the capacity of the network in terms of lowest communication latency, highest speed, enhanced throughput, minimum E2E delay, and minimizing the packet loss. In this paper, we discuss the working principle and key features of 5G communication technology along with the limitations of existing technologies. Further, we provide the taxonomy of the 5G network. Moreover, we provide a comparison of 5G and 4G LTE in terms of data privacy and security aspects. Further, we propose a four-layer architecture for ehealthcare system, which uses 5G NR (New Radio) architecture incorporating the control plane and user plane. We perform the simulation over the frequency range1 and frequency range2 and calculated the throughput and latency for distinct values of OFDM numerologies. Further, we provide a comparative analysis for 4G and 5G and deduce that 5G facilitates 10 times lower latency than 4G, and 5G can accommodate a much higher number of devices than 4G. In this work, we discuss providing better healthcare facilities electronically using 5G NR. Moreover, the data sharing and diagnosing the disease become faster and easier by using 5G NR.

An adaptive backoff and dynamic clear channel assessment mechanisms in IEEE 802.15.4 MAC for wireless body area networks

The low power connectivity and short distance communication qualities of the IEEE 802.15.4 standard suits its application in wireless body (personal) area networks (WBAN). IEEE 802.15.4 protocol use carrier sense multiple access and collision avoidance (CSMA-CA) medium access control (MAC) mechanism for aperiodic data communication; however, it faces performance degradation with a gradual increase in the number of network devices. Performance challenges arise due to; several heterogeneous and dynamic packet sources sharing limited resources during contention in a finite backoff period, association delay, and traffic channel access through clear channel assessment (CCA) algorithms. Due to such constraints, channel conditions increase packet collision and queuing delay, which raises power demand and impedes performance indicators, such as throughput, packet delivery ratio (PDR), and end to end (E2E) delay. This article proposes an alternative combination of the backoff and CCA adaptation mechanisms to improve WBAN performance characteristics in a highly dynamic environment considering network residue energy, PDR, throughput, and E2E delay. We use the Markov model to demonstrate a theoretical analysis of the proposed CSMA-CA MAC mechanism. A comparative study shows the proposed methods outperform conventional protocol in PDR, throughput, and reduced E2E delay by 13.1%, 21.44%, and 41.13%, respectively, where energy efficiency drops by 0.43%.

5G heterogeneous network (HetNets): a self-optimization technique for vertical handover management

PurposeVertical handover has been grown rapidly due to the mobility model improvements. These improvements are limited to certain circumstances and do not provide the support in the generic mobility, but offering vertical handover management in HetNets is very crucial and challenging. Therefore, this paper presents a vertical handoff management method using the effective network identification method.Design/methodology/approachThis paper presents a vertical handoff management method using the effective network identification method. The handover triggering schemes are initially modeled to find the suitable position for starting handover using computed coverage area of the WLAN access point or cellular base station. Consequently, inappropriate networks are removed to determine the optimal network for performing the handover process. Accordingly, the network identification approach is introduced based on an adaptive particle-based Sailfish optimizer (APBSO). The APBSO is newly designed by incorporating self-adaptive particle swarm optimization (APSO) in Sailfish optimizer (SFO) and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is utilized for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like end to end (E2E) delay, jitter, signal-to-interference-plus-noise ratio (SINR), packet loss, handover probability (HOP) are considered to find the best network.FindingsThe developed APBSO-based deep stacked autoencoder outperformed than other methods with a minimal delay of 11.37 ms, minimal HOP of 0.312, maximal stay time of 7.793 s and maximal throughput of 12.726 Mbps, respectively.Originality/valueThe network identification approach is introduced based on an APBSO. The APBSO is newly designed by incorporating self-APSO in SFO and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is used for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like E2E delay, jitter, SINR, packet loss and HOP are considered to find the best network. The developed APBSO-based deep stacked autoencoder outperformed than other methods with minimal delay minimal HOP, maximal stay time and maximal throughput.

Cooperative Learning for Disaggregated Delay Modeling in Multidomain Networks

Accurate delay estimation is one of the enablers of future network connectivity services, as it facilitates the application layer to anticipate network performance. If such connectivity services require isolation (slicing), such delay estimation should not be limited to a maximum value defined in the Service Level Agreement, but to a finer-grained description of the expected delay in the form of, e.g., a continuous function of the load. Obtaining accurate end-to-end (e2e) delay modeling is even more challenging in a multi-operator (Multi-AS) scenario, where the provisioning of e2e connectivity services is provided across heterogeneous multi-operator (Multi-AS or just domains) networks. In this work, we propose a collaborative environment, where each domain Software Defined Networking (SDN) controller models intra-domain delay components of inter-domain paths and share those models with a broker system providing the e2e connectivity services. The broker, in turn, models the delay of inter-domain links based on e2e monitoring and the received intra-domain models. Exhaustive simulation results show that composing e2e models as the summation of intra-domain network and inter-domain link delay models provides many benefits and increasing performance over the models obtained from e2e measurements.

Deep Reinforcement Learning Aided Packet-Routing for Aeronautical Ad-Hoc Networks Formed by Passenger Planes

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep reinforcement learning for routing in AANETs aiming at minimizing the end-to-end (E2E) delay. Specifically, a deep Q-network (DQN) is conceived for capturing the relationship between the optimal routing decision and the local geographic information observed by the forwarding node. The DQN is trained in an offline manner based on historical flight data and then stored by each airplane for assisting their routing decisions during flight. To boost the learning efficiency and the online adaptability of the proposed DQN-routing, we further exploit the knowledge concerning the system's dynamics by using a deep value network (DVN) conceived with a feedback mechanism. Our simulation results show that both DQN-routing and DVN-routing achieve lower E2E delay than the benchmark protocol, and DVN-routing performs similarly to the optimal routing that relies on perfect global information.

Design and Analysis of MEC- and Proactive Caching-Based $360^{\circ }$ Mobile VR Video Streaming

Recently, 360-degree mobile virtual reality video (MVRV) has become increasingly popular because it can provide users with an immersive experience. However, MVRV is usually recorded in a high resolution and is sensitive to latency, which indicates that broadband, ultra-reliable, and low-latency communication is necessary to guarantee the users’ quality of experience. In this paper, we propose a mobile edge computing (MEC)-based 360-degree MVRV streaming scheme with field-of-view (FoV) prediction, which jointly considers video coding, proactive caching, computation offloading, and data transmission. To meet the requirement of stringent end-to-end (E2E) latency, the user’s viewpoint prediction is utilized to cache video data proactively, and computing tasks are partially offloaded to the MEC server. In addition, we propose an analytical model based on diffusion process to study the packet transmission process of 360-degree MVRV in multihop wired/wireless networks and analyze the performance of the MEC-enabled scheme. The simulation results verify the accuracy of the analysis and the effectiveness of the proposed MVRV streaming scheme in reducing the E2E delay. Furthermore, the analytical framework sheds some light on the impacts of system parameters, e.g., FoV prediction accuracy and transmission rate, on the balance between computation delay and communication delay.

Performance Evaluation of the Proposed Wormhole Detection Scheme with Existing Schemes

When dynamic nature, autonomous, self- configured types nodes are combined together and make contact with each other for the purpose of communication and data sharing; these types of forms networks are known as a mobile ad hoc network. The nodes don't restrict themselves due to physical barrier. They move into any location, place and communicate with the other nodes within their communication range. There is no fixed location of nodes. The dynamic nature of MANET makes it vulnerable to different type of attack. We are considering the wormhole attack which affects the network by transferring the data from one location to another location using a long range tunnel. In this paper, we are evaluating the performance of the proposed scheme with existing schemes such as RTT1, RTT2, DelPHI. The performance of the proposed scheme is measured in terms of throughput, packet delivery ratio, packet loss, average E2E delay and jitter.

Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

Vehicular ad hoc network usually operates in various challenging situations like frequent topology changes, high vehicular mobility and the wide range of communication networks. Due to this it is very hard to maintain a higher data rate and also to achieve low latency during data communication. To overcome these problems, given the dynamic natures of all the vehicles in a given network in the proposed routing method, we have defined two fundamental parameters to determine the forwarding vehicle. The first parameter, which we developed, we call it “Channel quality factor (CQF)” or ‘Z’. The other parameter known as “Communication expiration time” or ‘T’ together with CQF is used in the present method to determine the forwarding vehicle. Fuzzy logic is also used to optimize various Quality of Service matrices. This proposed routing method involves two main parts; one is for forwarding Vehicle selection in the road based on the fuzzy logic. The second one is Road selection at the Road Junction to select the right path to reach the signal to the destination vehicle. The simulation results show that our proposed method performs well compare to other well-known protocols (MoZo, BRAVE, OFAODV) in terms of the average end to end delay, packet delivery ratio and control packet overhead, given any number of vehicles in a set of streets. While we are comparing with VEFR protocol, our proposed method shows higher performance in terms of average E2E delay and control packet overhead. However, it is interesting to see that VEFR gives $$\sim $$ 5% better result than our proposed method when the number of vehicles in the streets are lower. But in the limit, when the number of vehicles reaches close to $$\sim $$ 1900 the difference between the proposed method and method in VEFR goes to zero. At last we compare our proposed method with junction based two V2I protocols. In every cases, it shows better result even though we change the speed of the vehicles, beacon interval, channel data rate and transmission region.

Mobile ad-hoc network routing protocols of time-critical events for search and rescue missions

The most important experiences we discovered from several disasters are that cellular networks were vulnerable, and the loss of the communication system may have a catastrophic consequence. Mobile ad-hoc networks (MANETs) play a significant role in the construction of campus, resident, battlefield and search/rescue region. MANET is an appropriate network for supporting a communication where is no permanent infrastructure. MANET is an effective network that uses to establishing urgent communication between rescue members in critical situations like, disaster or natural calamities. The sending and receiving data in MANET is depending on the routing protocols to adapt the dynamic topology and maintain the routing information. Consequently, This paper evaluates the performance of three routing protocols in MANET: ad-hoc on-demand distance vector (AODV), destination sequenced distance vector (DSDV), and ad-hoc on-demand multipath distance vector (AOMDV). These protocols are inherent from different types of routing protocols: single-path, multi-path, reactive and proactive mechanisms. The NS2 simulator is utilized to evaluate the quality of these protocols. Several metrics are used to assess the performance of these protocols such: packet delivery ratio (PDR), packet loss ratios (PLR), throughput (TP), and end-to-end delay (E2E delay). The outcomes reveal the AOMDV is the most suitable protocol for time-critical events of search and rescue missions.

Study of Proactive Routing Protocol in Wildfire Detection Using Mobile Sensor Networks

ABSTRACT Mobile sensor networks include different routing protocols. The performance of these routing protocols differs in terms of transmitting and receiving the packets, where it depends on the architecture of each routing protocol. However, despite there are many protocols that have been proposed, but they have not implemented broadly in disasters, where there is a need to study the routing protocols in disasters such as forest fire detection. Therefore, this paper is present a proactive routing protocol called Optimized Link State Routing Protocol (OLSR). This protocol has implemented in the detection of forest fire based on a different number of mobile sensor nodes in the range of 10-50 nodes with an increment step of 10 nodes. Furthermore, the performance of the OLSR protocol is evaluated in terms of Packet Delivery Ratio (PDR), End-to-End Delay (E2E Delay), energy consumption, and routing overhead. The performance of the OLSR protocol shows encouraging results in the detection of forest fire.

Wireless Edge Machine Learning: Resource Allocation and Trade-Offs

The aim of this paper is to propose a resource allocation strategy for dynamic training and inference of machine learning tasks at the edge of the wireless network, with the goal of exploring the trade-off between energy, delay and learning accuracy. The scenario of interest is composed of a set of devices sending a continuous flow of data to an edge server that extracts relevant information running online learning algorithms, within the emerging framework known as Edge Machine Learning (EML). Taking into account the limitations of the edge servers, with respect to a cloud, and the scarcity of resources of mobile devices, we focus on the efficient allocation of radio (e.g., data rate, quantization) and computation (e.g., CPU scheduling) resources, to strike the best trade-off between energy consumption and quality of the EML service, including service end-to-end (E2E) delay and accuracy of the learning task. To this aim, we propose two different dynamic strategies: (i) The first method aims to minimize the system energy consumption, under constraints on E2E service delay and accuracy; (ii) the second method aims to optimize the learning accuracy, while guaranteeing an E2E delay and a bounded average energy consumption. Then, we present a dynamic resource allocation framework for EML based on stochastic Lyapunov optimization. Our low-complexity algorithms do not require any prior knowledge on the statistics of wireless channels, data arrivals, and data probability distributions. Furthermore, our strategies can incorporate prior knowledge regarding the model underlying the observed data, or can work in a totally data-driven fashion. Several numerical results on synthetic and real data assess the performance of the proposed approach.

Reliable and energy-aware job offloading at terahertz frequencies for mobile edge computing

In this paper, we co-design the transmission power and the offloading strategy for job offloading to a mobile edge computing (MEC) server at Terahertz (THz) frequencies. The goal is to minimize the communication energy consumption while providing ultra-reliable low end-to-end latency (URLLC) services. To that end, we first establish a novel reliability framework, where the end-to-end (E2E) delay equals a weighted sum of the local computing delay, the communication delay and the edge computing delay, and the reliability is defined as the probability that the E2E delay remains below a certain pre-defined threshold. This reliability gives a full view of the statistics of the E2E delay, thus constituting advancement over prior works that have considered only average delays. Based on this framework, we establish the communication energy consumption minimization problem under URLLC constraints. This optimization problem is non-convex. To handle that issue, we first consider the special single-user case, where we derive the optimal solution by analyzing the structure of the optimization problem. Further, based on the analytical result for the single-user case, we decouple the optimization problem for multi-user scenarios into several sub-optimization problems and propose a sub-optimal algorithm to solve it. Numerical results verify the performance of the proposed algorithm.

Advanced GPSR in Mobile Ad-hoc Networks (MANETs)

This work aims to develop the routing capability of GPSR in MANETs. A new GPSR advancement described as Adaptive GPSR (AGPSR) is proposed with enhanced greedy forwarding and efficient routing decision. AGPSR greedy forwarding model consists of three major phases; initialization, finding target neighbor, weight value computation and next hop selection. The weight value encounters a set of network metrics including node density, network size, congestion level, transmission range, node speed and movement direction. An intensive evaluation methodology was implemented in order to evaluate the performance of proposed AGPSR in MANET. Results confirm that proposed GPSR has surpassed several MANET environmental challenges and outperformed conventional GPSR in terms of PDR, E2E delay, routing overhead, and power consumption. The delay is reduced by AGPSR of up-to 10% compared to conventional GPSR. In addition, 5% increase in PDR and more than 7% decrease in routing overhead and in power consumption was achieved by AGPSR.

Joint DNN Partition Deployment and Resource Allocation for Delay-Sensitive Deep Learning Inference in IoT

Nowadays, the widely used Internet-of-Things (IoT) mobile devices (MDs) generate huge volumes of data, which need analyzing and extracting accurate information in real time by compute-intensive deep learning (DL) inference tasks. Due to its multilayer structure, the deep neural network (DNN) is appropriate for the mobile-edge computing (MEC) environment, and the DL tasks can be offloaded to DNN partitions deployed in MEC servers (MECSs) for speed-up inference. In this article, we first assume the arrival process of DL tasks as Poisson distribution and develop a tandem queueing model to evaluate the end-to-end (E2E) inference delay of DL tasks in multiple DNN partitions. To minimize the E2E delay, we develop a joint optimization problem model of partition deployment and resource allocation in MECSs (JPDRA). Since the JPDRA is a mixed-integer nonlinear programming (MINLP) problem, we decompose the original problem into a computing resource allocation (CRA) problem with fixed partition deployment decision and a DNN partition deployment (DPD) problem that optimizes the optimal-delay function related to the CRA problem. Next, we design a CRA algorithm based on Markov approximation and a low-complexity DPD algorithm to obtain the near-optimal solution in the polynomial time. The simulation results demonstrate that the proposed algorithms are more efficient and can reduce the average E2E delay by 25.7% with better convergence performance.

An Energy-Aware Data Transmission Scheme under the Guarantee of Reliability for 3D WSNs

Three-dimensional wireless sensor networks (3D WSNs) play an important role to provide data collection services for Internet of things (IoT) in the real applications. However, many of the existing WSN data collection researches are based on a relatively simple linear or plane network model. The three-dimensional space problems are simplified to two-dimensional plane, which limits the applicability. In this paper, the data collection in 3D WSN is studied. In the three-dimensional space, we firstly analyze the data loads, energy consumption, and end-to-end (E2E) delay of each node when the network is following the shortest path routing. The mathematical analysis of data loads and E2E delay of each node are presented. Based on the analysis of data loads and energy consumption, an energy-ware data transmission scheme is proposed to achieve the trade-off optimization between the E2E delay and network lifetime under the guarantee of the transmission reliability. The key point of the proposed scheme is to make fully use of the unbalanced energy consumption of the 3D WSN. The performance of the proposed scheme is discussed, analyzed, and evaluated. The theoretical analysis and simulation results show that the E2E network delay and energy efficiency can be improved under the constraint of transmission reliability.