Showing papers on "Packet loss published in 2019"

Energy-Efficient Algorithm for Reliable Routing of Wireless Sensor Networks

Abstract: Quality of service (QoS) routing is one of the critical challenges in wireless sensor networks (WSNs), especially for surveillance systems. Multihop data transmission of WSNs, due to the high packet loss and energy-efficiency, requires reliable links for end-to-end data delivery. Current multipath routing works can provision QoS requirements like end-to-end reliability and delay, but suffer from a significant energy cost. To improve the efficiency of the network with multiconstraints QoS parameters, in this paper we model the problem as a multiconstrained optimal path problem and propose a distributed learning automaton (DLA) based algorithm to preserve it. The proposed approach leverages the advantage of DLA to find the smallest number of nodes to preserve the desired QoS requirements. It takes several QoS routing constraints like end-to-end reliability and delay into account in path selection. We simulate the proposed algorithm, and the obtained results verify the effectiveness of our solution. The results demonstrate that our algorithm has a better performance than current state-of-the-art competitive algorithms in terms of end-to-end delay and energy-efficiency.

Quality of Service Optimization in an IoT-Driven Intelligent Transportation System

Abstract: High mobility in ITS, especially V2V communication networks, allows increasing coverage and quick assistance to users and neighboring networks, but also degrades the performance of the entire system due to fluctuation in the wireless channel. How to obtain better QoS during multimedia transmission in V2V over future generation networks (i.e., edge computing platforms) is very challenging due to the high mobility of vehicles and heterogeneity of future IoT-based edge computing networks. In this context, this article contributes in three distinct ways: to develop a QoS-aware, green, sustainable, reliable, and available (QGSRA) algorithm to support multimedia transmission in V2V over future IoT-driven edge computing networks; to implement a novel QoS optimization strategy in V2V during multimedia transmission over IoT-based edge computing platforms; to propose QoS metrics such as greenness (i.e., energy efficiency), sustainability (i.e., less battery charge consumption), reliability (i.e., less packet loss ratio), and availability (i.e., more coverage) to analyze the performance of V2V networks. Finally, the proposed QGSRA algorithm has been validated through extensive real-time datasets of vehicles to demonstrate how it outperforms conventional techniques, making it a potential candidate for multimedia transmission in V2V over self-adaptive edge computing platforms.

Uplink Grant-Free Access Solutions for URLLC services in 5G New Radio

Abstract: Ultra-reliable low latency communication requires innovative resource management solutions that can guarantee high reliability at low latency. Grant-free random access, where channel resources are accessed without undergoing assignment through a handshake process, is proposed in 5G New Radio as an important latency reducing solution. However, this comes at an increased likelihood of collisions resulting from uncoordinated channel access. Novel reliability enhancement techniques are therefore needed. This article provides an overview of grant-free random access in 5G New Radio focusing on the ultra-reliable low latency communication service class, and presents two reliability-enhancing solutions. The first proposes retransmissions over shared resources, whereas the second proposal incorporates grant-free transmission with non-orthogonal multiple access where overlapping transmissions are resolved through the use of advanced receivers. Both proposed solutions result in significant performance gains, in terms of reliability as well as resource efficiency. For example, the proposed non-orthogonal multiple access scheme can support a normalized load of more than 1.5 users/slot at packet loss rates of ~ 10−5 a significant improvement over conventional grant-free schemes like slotted-ALOHA.

AoI-Penalty Minimization for Networked Control Systems with Packet Loss

Abstract: In this work the impact of age of information (AoI) is studied from the perspective of networked control systems (NCS), i.e., control loops that are closed over networks. We formulate the estimation problem of a linear time invariant (LTI) system and show that related performance metrics can be optimized by minimizing age-penalty functions. From the variety of possible penalties that make sense from an NCS point of view, we derive a general age-penalty minimization problem. We characterize properties of penalty functions that are trivial or non-trivial to solve and show that for non-trivial age-penalties, the optimal transmission policy over a single link with packet loss is AoI-threshold based. Then, we propose an algorithm to find the optimal threshold. Simulation results verify that threshold policies with optimal threshold can serve to optimally solve a variety of NCS related estimation problems.

Secure Trust-Based Blockchain Architecture to Prevent Attacks in VANET.

Abstract: Vehicular ad hoc networks (VANET) are also known as intelligent transportation systems. VANET ensures timely and accurate communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve road safety and enhance the efficiency of traffic flow. Due to its open wireless boundary and high mobility, VANET is vulnerable to malicious nodes that could gain access into the network and carry out serious medium access control (MAC) layer threats, such as denial of service (DoS) attacks, data modification attacks, impersonation attacks, Sybil attacks, and replay attacks. This could affect the network security and privacy, causing harm to the information exchange within the network by genuine nodes and increase fatal impacts on the road. Therefore, a novel secure trust-based architecture that utilizes blockchain technology has been proposed to increase security and privacy to mitigate the aforementioned MAC layer attacks. A series of experiment has been conducted using the Veins simulation tool to assess the performance of the proposed solution in the terms of packet delivery ratio (PDR), end-to-end delay, packet loss, transmission overhead, and computational cost.

SmartCC: A Reinforcement Learning Approach for Multipath TCP Congestion Control in Heterogeneous Networks

Abstract: The Multipath TCP (MPTCP) protocol has been standardized by the IETF as an extension of conventional TCP, which enables multi-homed devices to establish multiple paths for simultaneous data transmission. Congestion control is a fundamental mechanism for the design and implementation of MPTCP. Due to the diverse QoS characteristics of heterogeneous links, existing multipath congestion control mechanisms suffer from a number of performance problems such as bufferbloat, suboptimal bandwidth usage, etc. In this paper, we propose a learning-based multipath congestion control approach called SmartCC to deal with the diversities of multiple communication path in heterogeneous networks. SmartCC adopts an asynchronous reinforcement learning framework to learn a set of congestion rules, which allows the sender to observe the environment and take actions to adjust the subflows’ congestion windows adaptively to fit different network situations. To deal with the problem of infinite states in high-dimensional space, we propose a hierarchical tile coding algorithm for state aggregation and a function estimation approach for $Q$ -learning, which can derive the optimal policy efficiently. Due to the asynchronous design of SmartCC, the processes of model training and execution are decoupled, and the learning process will not introduce extra delay and overhead on the decision making process in MPTCP congestion control. We conduct extensive experiments for performance evaluation, which show that SmartCC improves the aggregate throughput significantly and outperforms the state-of-the-art mechanisms on a variety of performance metrics.

On-Board Deep Q-Network for UAV-Assisted Online Power Transfer and Data Collection

Abstract: Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT) capability provide a practical means to deploy a large number of wireless powered sensing devices into areas with no access to persistent power supplies. The UAV can charge the sensing devices remotely and harvest their data. A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV. In this paper, an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices, by optimally deciding the device to be charged and interrogated for data collection, and the instantaneous patrolling velocity of the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the states of battery level and data queue length of devices, channel conditions, and waypoints given the trajectory of the UAV; and solve it optimally with Q-learning. Furthermore, we propose the on-board deep Q-network that enlarges the state space of the MDP, and a deep reinforcement learning based scheduling algorithm that asymptotically derives the optimal solution online, even when the UAV has only outdated knowledge on the MDP states. Numerical results demonstrate that our deep reinforcement learning algorithm reduces the packet loss by at least 69.2%, as compared to existing non-learning greedy algorithms.

Segment Routing in Software Defined Networks: A Survey

Abstract: Segment routing (SR) has emerged as a promising source-routing methodology to overcome the challenges in the current routing schemes It has received noticeable attention both in industry and academia, due to its flexibility, scalability, and applicability, especially in software defined networks The emerging cloud services require strict service level agreements such as packet loss, delay, and jitter Studies have shown that traditional network architectures lack the essential flexibility and scalability to offer these services To combat this, a more flexible and agile routing paradigm of SR enables a source node to steer an incoming packet along a performance engineered path represented as an ordered list of instructions called segment list This is encoded as a multiprotocol label switching label stack or an IPv6 address list in the packet header This paper provides a comprehensive review of the novel SR technology by describing its architecture, operations, and key applications to date SR paradigm can be effectively applied to a wide range of network applications, such as traffic engineering, network resiliency, network monitoring, and service function chaining, to achieve efficient network solutions Furthermore, this paper identifies an interesting set of future research directions and open issues that can help realize the full potential of the emergent SR paradigm

Energy-Efficient Multicast Routing Protocol based on SDN and Fog Computing for Vehicular Networks

Abstract: Vehicular networks have been expanding significantly to perform several applications and strategies related to vehicles, ambulances, traffic jam, drivers, and even passengers. The most important challenge in this network is routing data among vehicles. Therefore, there is a need to design efficient routing protocols for unicast, Geocast, multicast and broadcast transmission modes. The multicasting can be used in many application fields such as emergency, police, and firefighting. There is a large body of studies on multicast routing in vehicular networks. However, safety applications in vehicular networks require a special multicast routing protocol that takes into account the deadline and existing bandwidth constraints. On the other hand, there has been a growing tendency towards electric cars in recent years. Therefore, energy consumption is one of major parameters that should be considered in the design of this routing protocol. The goal of this paper is to present a new E nergy E fficient M ulticast routing protocol based on S oftware Defined Networks and F og computing for V ehicular networks called EEMSFV including deadline and bandwidth constraints. Multicast routing with multiple constraints of QoS has been proved to be a NP complete problem. The proposed architecture consists of four layers: vehicles, fog computing, OpenFlow switches and SDN controller. Moreover, a priority based scheduling algorithm and a classification algorithm to schedule the multicast requests based on their application type and deadline constraint after classifying them are proposed. The partitioning concept is used to decrease time complexity and overhead in the SDN controller. From the simulation results, we concluded that EEMSFV is better than MABC and CVLMS in terms of successfully transmitted ratio, average end to end delay, normalized overhead load, multicast energy consumption, packet loss ratio and percentage of critical multicast sessions that meet the deadline.

A low redundancy data collection scheme to maximize lifetime using matrix completion technique

Abstract: Sensor nodes equipped with various sensory devices can sense a wide range of information regarding human or things, thereby providing a foundation for Internet of Thing (IoT). Fast and energy-efficient data collection to the control center (CC) is of significance yet very challenging. To deal with this challenge, a low redundancy data collection (LRDC) scheme is proposed to reduce delay as well as energy consumption for monitoring network by using matrix completion technique. Due to the correlation of the location-dependent sensing data, some data without being collected can still be recovered by the matrix completion technology, thereby reducing the data amount for data collection and transmission, reducing the network energy consumption, and accelerating the process of data acquisition. Based on matrix completion technique, LRDC scheme can select only part of the nodes to sense data and transmit less data to CC. By doing so, the data collected by the network can be greatly reduced, which can effectively improve the network lifetime. In addition, LRDC scheme also proposes a method for quickly compensate sample data in cases of packet loss, whereby part of redundant data is sent in advance to the area closer to CC. If the data required for matrix completion is lost, these redundant data can be quickly obtained by CC, so the LRDC scheme has low delay characteristics. Simulation results demonstrate that LRDC scheme can achieve better performance than the traditional strategy, and it can reduce the maximum energy consumption of the network by 27.6–57.9% and reduce the delay by 0.7–17.9%.

String Stable Model Predictive Cooperative Adaptive Cruise Control for Heterogeneous Platoons

Abstract: Cooperative adaptive cruise control (CACC) is a potential solution to decrease traffic jams caused by shock waves, increase the road capacity, decrease fuel consumption and improve safety. This paper proposes an integrated solution to a combination of four challenges in these CACC systems. One of the technological challenges is how to guarantee string stability (the ability to avoid amplification of dynamic vehicle responses along the string of vehicles) under nominal operational conditions. The second challenge is how to apply this solution to heterogeneous vehicles. The third challenge is how to maintain confidentiality of the vehicle parameters. Finally, the fourth challenge is to find a method which improves robustness against wireless packet loss. This paper proposes a model predictive control approach in combination with a feed-forward control design, which is based on a shared vector of predicted accelerations over a finite time horizon. This approach is shown to be applicable to a heterogeneous sequence of vehicles, while the vehicle parameters remain confidential. In previous works such an approach has shown to increase robustness against packet losses. Conditions for string stability are presented for the nominal operational conditions. Experimental results are presented and indeed demonstrate string stable behavior.

Efficient Path Planning for a Mobile Sink to Reliably Gather Data from Sensors with Diverse Sensing Rates and Limited Buffers

Abstract: Wireless sensor networks are vulnerable to energy holes, where sensors close to a static sink are fast drained of their energy. Using a mobile sink (MS) can conquer this predicament and extend sensor lifetime. How to schedule a traveling path for the MS to efficiently gather data from sensors is critical in performance. Some studies select a subset of sensors as rendezvous points (RPs). Non-RP sensors send data to the nearest RPs and the MS visits RPs to retrieve data. However, these studies assume that sensors produce data with the same speed and have no limitation on buffer size. When the two assumptions are invalid, they may encounter serious packet loss due to buffer overflow at RPs. In the paper, we show that the path planning problem is NP-complete and propose an efficient path planning for reliable data gathering (EARTH) algorithm by relaxing these impractical assumptions. It forms a spanning tree to connect all sensors and then selects each RP based on hop count and distance in the tree and the amount of forwarding data from other sensors. An enhanced EARTH (eEARTH) algorithm is also developed to further reduce path length. Both EARTH and eEARTH incur less computational overhead and can flexibly recompute new paths when sensors change sensing rates. Simulation results verify that they can find short traveling paths for the MS to collect sensing data without packet loss, as compared with existing methods.

Enhanced intrusion detection and prevention system on cloud environment using hybrid classification and OTS generation

Abstract: Cloud environment is an assembly of resources for furnishing on-demand services to cloud customers. Here access to cloud environment is via internet services in which data stored on cloud environment are easier to both internal and external intruders. To detect intruders, various intrusion detection systems and authentication systems was proposed in earlier researches which are primarily ineffective. Many existing researchers were concentrated on machine learning approaches for detecting intrusions using fuzzy clustering, artificial neural network, support vector machine, fuzzy with neural network and etc., which are not furnishing predominant results based on detection rate and false negative rates. Our proposed system directed on intrusion detection system and it uses cloudlet controller, trust authority and virtual machine management in cloud environment. We propose two novel algorithms such as (i) packet scrutinization algorithm which examines the packets from the users and (ii) hybrid classification model called “NK-RNN” which is a combination of normalized K-means clustering algorithm with recurrent neural network. For preventing the user from intruders, we propose a one time signature for cloud user in order to access the data on cloud environment. Our proposed classifier effectively detects the intruders which are experimentally proved by comparing with existing classification models. Thus our proposed results are expressed by packet loss ratio, average packet delay, throughput, detection rate, false positive rate and false negative rate.

Efficient energy optimization in mobile ad hoc network (MANET) using better-quality AODV protocol

Abstract: Mobile Ad hoc Network (MANET) is a collection of nodes formed together to make communication without need of infrastructure. Due to restricted characteristics of MANET has to relay on wireless communication. This causes the battery power is an important factor in MANET to provide reliable communication without any power failure. Many research works has been carried out to improve the battery power to avoid packet dropping or packet loss while transmitting of packets. The battery power optimization in MANET is still not come in to the wrapping up. This research article proposed a method to improve the battery power in MANET and provide the better quality in packet transmission. This is achieved by using AODV protocol with improving the routing strategy in packet transmission. The result was implemented in Network simulator and shows better improvement comparing with the existing power optimization strategy.

A Survey of Delay-Based and Hybrid TCP Congestion Control Algorithms

Abstract: Congestion control (CC) has a significant influence on the performance of transmission control protocol (TCP) connections. Over the last three decades, many researchers have extensively studied and proposed a multitude of enhancements to standard TCP CC. However, this topic still inspires both academic and industrial research communities due to the change in Internet application requirements and the evolution of Internet technologies. The standard TCP CC infers network congestion based on packet loss events which leads to long queuing delay when bottleneck buffer size is large. A promising solution to this problem is to use the delay signal (RTT or one-way delay measurements) to infer congestion earlier and react to the congestion before the queuing delay reaches a high value. In this survey paper, we describe the delay signal and the algorithms that completely or partially utilize this type of signal. Additionally, we illustrate standard CC and modern active queue management (AQM) principles and discus the interaction between AQM and the delay signal.

Toward Ultrareliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues

Abstract: Ultrareliable low-latency communications (URLLC) is one of three emerging application scenarios in 5G new radio (NR) for which physical layer design aspects have been specified. With 5G NR, we can guarantee reliability and latency in radio access networks. However, for communication scenarios where the transmission involves both radio access and wide-area core networks, the delay in radio access networks contributes to only a portion of the end-toend (E2E) delay. In this article, we outline the delay components and packet loss probabilities in typical URLLC scenarios and formulate the constraints on E2E delay and overall packet loss probability. Then, we summarize possible solutions in the physical, link, and network layers as well as the cross-layer design. Finally, we discuss open issues in prediction and communication codesign for URLLC in wide-area, largescale networks.

Non-intrusive Speech Quality Assessment for Super-wideband Speech Communication Networks

Abstract: The quality of speech communication networks has recently improved significantly by extending the available audio bandwidth from narrowband, firstly to wideband, and then to super-wideband. This bandwidth extension marks the end of the typically muffled sound we know from plain old telephone services. Another reason for increased speech quality is the fully digitally packet-based transmission. However, so far, no speech quality prediction model is able to estimate super-wideband quality without a clean reference signal. In this paper, we present a non-intrusive speech quality assessment model NISQA, which – in contrast to current state-of-the-art models – can predict the quality of super-wideband speech transmission. Furthermore, it is able to accurately predict the quality impact of packet loss concealment of modern codecs, such as Opus and EVS. The model uses a novel approach, where a CNN firstly estimates the per-frame quality, and subsequently, an RNN aggregates the per-frame values over time, to estimate the overall speech quality. Averaged over a comprehensive test set, the model achieves an RMSE*3rd of 0.29 with subjective MOS.

A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System

Abstract: Emerging revolution in the healthcare has caught the attention of both the industry and academia due to the rapid proliferation in the wearable devices and innovative techniques. In the mean-time, body sensor networks (BSNs) have become the potential candidate in transforming the entire landscape of the medical world. However, large battery lifetime and less power drain are very vital for these resource-constrained sensor devices while collecting the bio signals. Hence, minimizing their charge and energy depletions are still very challenging tasks. It is examined through large real-time data sets that due to the dynamic nature of the wireless channel, the traditional predictive transmission power control (TPC) and a constant transmission power techniques are no more supportive and potential candidates for BSNs. Thus, this paper first proposes a novel joint TPC and duty-cycle adaptation-based framework for pervasive healthcare. Second, an adaptive energy-efficient transmission power control algorithm is developed by adapting the temporal variation in the on-body wireless channel amid static (i.e., standing and walking at a constant speed) and dynamic (i.e., running) body postures. Third, a feedback control-based duty-cycle algorithm is proposed for adjusting the execution period of tasks (i.e., sensing and transmission). Fourth, system-level battery and energy harvesting models are proposed for body sensor nodes by examining the energy depletion of sensing and transmission tasks. It is validated through Monte Carlo experimental analysis that the proposed algorithm saves more energy of 11.5% with reasonable packet loss ratio by adjusting both the transmission power and the duty cycle unlike the conventional constant TPC and PTPC methods.

STFANET: SDN-Based Topology Management for Flying Ad Hoc Network

Abstract: In recent years, with the growth in the use of Unmanned Aerial Vehicles (UAVs), UAV-based systems have become popular in both military and civil applications. The lack of reliable communication infrastructure in these scenarios has motivated the use of UAVs to establish a network as flying nodes, also known as Flying Ad Hoc Networks (FANETs). However, the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology, which makes more challenging to guarantee their communication during the operational time. In this context, this article presents a Software-defined networking (SDN) based Topology management for FANETs - called of STFANET -, which is a coordination protocol that englobes both an efficient SDN-based UAV communication and a set of topology management algorithms. The goal is to establish and maintain a FANET topology in order to provide a constant and reliable communication link among independent nodes - which are performing individual or collaborative missions - through relays units. Simulation results show the efficiency of the proposed protocol in order to provide communication in a dynamic scenario. Considering its use in a military setting, STFANET managed to achieve 25% of packet loss in transmitting data packets, 1.5ms of latency and 71% of connectivity on average.

Interference-Aware Multisource Transmission in Multiradio and Multichannel Wireless Network

Abstract: A node can provide a file to other nodes after downloading the file or data from the Internet. When more than one node have obtained the same file, this is considered a multisource transmission, in which all these nodes can act as candidate providers (sources) and transmit the file to a new request node (destination) together. In cases where there is negligible or no interference, multisource transmission can improve the download throughput because of parallel transmissions through multiple paths. However, this improvement is not guaranteed due to wireless interference among different paths. Wireless interference can be alleviated by the multiradio and multichannel technique. Because the source and multipath routing selections interact with channel assignment, the multisource transmission problem with multiradio and multichannel presents a significant challenge. In this paper, we propose a distributed joint source, routing, and channel selection scheme. The source selection issue can be concurrently solved via multipath finding. There are three sub-algorithms in our scheme, namely, interference-aware routing algorithm, channel assignment algorithm, and local routing adjustment algorithm. The interference-aware routing algorithm is used to find paths sequentially and is jointly executed with the channel assignment algorithm. After finding a new path, the local routing adjustment algorithm may be executed to locally adjust the selected paths so as to further reduce wireless interference. Extensive simulations have been conducted to demonstrate that our algorithms can effectively improve the network aggregate throughput, as well as reduce delay and packet loss probability.

Vehicle-to-Vehicle Communications for Platooning: Safety Analysis

Abstract: Vehicle-to-vehicle (V2V) communication is the key technology enabling platooning. This letter proposes an analytical framework that combines the characteristics of V2V communication (packet loss probabilities and packet transmission delays) with the physical mobility characteristics of vehicles (speed, distance between vehicles and their brake capacities). First, we present the feasible region of communications delays which guarantees safe emergency braking in platooning scenarios. Second, we derive a bound on the probability of safe braking. The presented framework is applied to understand the performance of the state-of-the-art V2V communication protocol for platooning.

Energy efficient routing formation algorithm for hybrid ad-hoc network: A geometric programming approach

Abstract: In this paper, a novel routing formation algorithm called Geometric programming based Energy Efficient Routing protocol (GEER) is proposed for hybrid ad-hoc network. It optimizes two sets of objectives: (i) maximize network lifetime and throughput, and (ii) minimize packet loss and routing overhead. The stated optimizations are done by the fusion of multi-objective optimization, geometric programming, and intuitionistic fuzzy set. The combination of stated techniques provides an effective tool that evaluates an optimal solution based on all objectives and estimates non-linear parameters of the network. The proposed method GEER is simulated in LINGO optimization software and validated with some existing methods in several scenarios. The outcomes of validation illustrate that the proposed method GEER outperforms the other existing methods based on several network metrics.

RPL Routing Protocol Performance in Smart Grid Applications Based Wireless Sensors: Experimental and Simulated Analysis

Abstract: The Advanced Metering Infrastructure (AMI) is one of the Smart Grid (SG) applications that used to upgrade the current power system by proposing a two-way communication system to connect the smart meter devices at homes with the electric control company. The design and deployment of an efficient routing protocol solution for AMI systems are considered to be a critical challenge due to the constrained resources of the smart meter nodes. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was recently standardized by the IETF and originally designed to satisfy the routing requirements of lossy and low power networks like wireless sensors (WSN). We have two kinds of AMI applications, on one hand AMI based WSN and on the other hand AMI based PLC communication. In this paper, we proposed a real and simulated implementation of RPL behavior with proper modifications to support the AMI based WSN routing requirements. We evaluate RPL performance using 140 nodes from the wireless sensor testbed (IoT-LAB) and 1000 nodes using Cooja simulator measure RPL performance within medium and high-density networks. We adopted two routing metrics for path selection: First one is HOP Count (HC) and the second is Expected Transmission Unit (ETX) to evaluate RPL performance in terms of packet delivery ratio; network latency; control traffic overhead; and power consumption. Our results illustrate that routes with ETX calculations in low and medium network densities outperform routes using HC and the performance decreases as the network becomes dense. However, Cooja implementation results provides an average reasonable performance for AMI with high-density networks; still many RPL nodes suffering from high packet loss rates, network congestion and many retransmissions due to the selection of optimal paths with highly unreliable links.

Congestion Control in V2V Safety Communication: Problem, Analysis, Approaches

Abstract: The emergence of Vehicular Ad Hoc Networks (VANETs) is expected to be an important step toward achieving safety and efficiency in intelligent transportation systems (ITS). One important requirement of safety applications is that vehicles are able to communicate with neighboring vehicles, with very low latency and packet loss. The high mobility, unreliable channel quality and high message rates make this a challenging problem for VANETs. There have been significant research activities in recent years in the development of congestion control algorithms that ensure reliable delivery of safety messages in vehicle-to-vehicle (V2V) communication. In this paper, we present a comprehensive survey of congestion control approaches for VANET. We identify the relevant parameters and performance metrics that can be used to evaluate these approaches and analyze each approach based a number of factors such as the type of traffic, whether it is proactive or reactive, and the mechanism for controlling congestion. We conclude this paper with some additional considerations for designing V2V communication protocols and interesting and open research problems and directions for future work.

Cross-Layer Design for Mission-Critical IoT in Mobile Edge Computing Systems

Abstract: In this paper, we establish a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for mission-critical Internet-of-Things (MC-IoT) services with short packets in mobile edge computing (MEC) systems, where enhanced mobile broadband (eMBB) services with long packets are considered as background services. To reduce communication delay, the fifth generation new radio is adopted in radio access networks. To avoid long queueing delay for short packets from MC-IoT, processor-sharing (PS) servers are deployed at MEC systems, where the service rate of the server is equally allocated to all the packets in the buffer. We derive the distribution of latency experienced by short packets in closed form, and minimize the overall packet loss probability subject to the end-to-end delay requirement. To solve the nonconvex optimization problem, we propose an algorithm that converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios. Furthermore, we derive the optimal solutions in two asymptotic cases: communication or computing is the bottleneck of reliability. The simulation and numerical results validate our analysis and show that the PS server outperforms first-come-first-serve servers.

Sender-Receiver Role-Based Energy-Aware Scheduling for Internet of Underwater Things

Abstract: The advance of Internet-of-Things (IoT) has extended its concept to underwater environments. The networks of underwater sensors and smart interconnected underwater objects have become an integral part of the IoT ecosystem as the Internet of Underwater Things (IoUT). This paper focuses on the problem of providing a scheduling service to support the transmission of sensory data of these smart underwater objects with high computation-utilization and high energy-efficiency. We design and implement a sender-receiver role-based scheduling protocol for Energy-Aware scheduling with Spatial-Temporal reuse, called EAST. Our EAST protocol is unique in three aspects. First, we introduce a probability-based contending model to address the hidden and exposed terminal problems. Second, we explore fine granularity reuse opportunities by introducing a sender-receiver role-based spatial and temporal reuse optimization and a multifactorial state transition mechanism to regulate the engagement status of each node. Third but not the least, the EAST protocol addresses the known uncertainty problem of packet loss by building a sender-initiated behavior model using Prospect Theory. We evaluate EAST through extensive experiments and show that our EAST protocol outperforms existing representative MAC protocols in terms of network throughput, delivery success ratio and energy consumption.

Predictive Control for Networked Interval Type-2 T–S Fuzzy System via an Event-Triggered Dynamic Output Feedback Scheme

Abstract: In this paper, the problem of event-triggered dynamic output feedback model predictive control (OFMPC) for nonlinear networked control systems (NCSs) with packet loss and bounded disturbance is studied. Interval type-2 (IT2) Takagi–Sugeno fuzzy model is exploited to represent the nonlinear plant with parameter uncertainties, which can be captured by the lower and upper membership functions. Whether or not the measured output should be released into unreliable network links is determined by the error between the current measured output and the latest event-triggered output. The Bernoulli random binary distribution is used to describe the process of packet loss in NCSs. This paper proposes the following: 1) the synthesis approach, including the design of the parameter-dependent dynamic output feedback controller by solving an online MPC optimization problem, which minimizes the upper bound of an infinite time horizon quadratic objective function respecting input and state constraints; 2) the guarantee of recursive feasibility and quadratical stability of the closed-loop system by applying the quadratic boundedness technique. Moreover, an algorithm of tightening the ellipsoidal bounds of state error is added to improve the control performance. The simulation and comparison studies are performed to demonstrate the usefulness and availability of the presented new techniques.

Adaptive Energy Aware Quality of Service for Reliable Data Transfer in Under Water Acoustic Sensor Networks

Abstract: Currently, reliable data transfer, and energy management have been considered as a significant research challenge in the underwater acoustic sensor networks (UWASN) owing to high packet loss, limited ratio of bandwidth with significant incur of energy, network life time with high propagation delay, less precision with high data hold time and so on. Energy saving and maintaining quality of service (QoS) is more important for UWASN owing to QoS application necessity and limited sensor nodes. To address this issue, several existing algorithms such as adaptive data forwarding algorithms, QoS-based congestion control algorithms and several methodologies were proposed with high throughput and less network lifetime as well as the less utilization of energy in UWASN by choosing sensor nodes data based on data transfer and link reliability. However, all the conventional algorithms have fixed data hold time, which incurs more end-to-end delay with less reliability of data and consumption of high energy due to high data transfer reachability. This high end research proposes adaptive energy aware quality of service (AEA-QoS) algorithm for reliable data delivery by formulating discrete times stochastic control process and deep learning techniques for UWSAN to overcome these issues. The proposed algorithm has been validated with conventional state-of-the-art methods and results show that the proposed approach exhibits its effectiveness in terms of less network overhead and propagation delay with high throughput and less energy consumption for every reliable packet transmission.