Packet Loss Probability Research Articles

Optimal Operational Parameters for 5G Energy Harvesting Cognitive Wireless Sensor Networks

ABSTRACTDense communication networks have been investigated recently to enable communications of low power wireless devices such as Internet-of-things (IoT) applications in the fifth cellular generation (5G). The wireless sensor network (WSN) is one of the principal technologies of IoT as it plays a critical role in numerous industries such as agriculture, healthcare, and environmental applications. Despite the advantages of the WSN, it is yet difficult to be deployed due to the scarcity of the radio spectrum with the increasing popularity of wireless applications. Therefore, merging of two technologies WSN and cognitive radio network (CRN), as cognitive radio wireless sensor network (CR-WSN), became essential for IoT applications. Another major challenge in such systems is the power constrain and delay sensitivity in such numerous wireless devices. Radio-frequency energy harvesting (EH) capability is supposed to merge with such systems in order to efficiently power and enhance the overall system energy. Thus, this paper discusses a CR-WSN model based on EH in a non-ordinary M/M/1 Markovian battery model with the proposal of a frame structure of the wireless node's charging and sensing time. The contribution of the power obtained from harvesting is derived with the proposal of a realistic power required/harvested model in RF EH CRN. Moreover, the power efficiency of the CR-WSN model is calculated and the derivation of the transmission delay is introduced in the same model. Furthermore, combined optimization of the required power, probability of packet loss and the transmission delay is proposed to validate the overall system performance with the recommended system operational parameters.

A robust fractional-order PID controller design based on active queue management for TCP network

ABSTRACTIn this paper, a robust fractional-order controller is designed to control the congestion in transmission control protocol (TCP) networks with time-varying parameters. Fractional controllers can increase the stability and robustness. Regardless of advantages of fractional controllers, they are still not common in congestion control in TCP networks. The network parameters are time-varying, so the robust stability is important in congestion controller design. Therefore, we focused on the robust controller design. The fractional PID controller is developed based on active queue management (AQM). D-partition technique is used. The most important property of designed controller is the robustness to the time-varying parameters of the TCP network. The vertex quasi-polynomials of the closed-loop characteristic equation are obtained, and the stability boundaries are calculated for each vertex quasi-polynomial. The intersection of all stability regions is insensitive to network parameter variations, and results in robust stability of TCP/AQM system. NS-2 simulations show that the proposed algorithm provides a stable queue length. Moreover, simulations show smaller oscillations of the queue length and less packet drop probability for FPID compared to PI and PID controllers. We can conclude from NS-2 simulations that the average packet loss probability variations are negligible when the network parameters change.

Sampled-Data Consensus of Linear Multi-agent Systems With Packet Losses.

In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.

Smart Collaborative Caching for Information-Centric IoT in Fog Computing.

The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN) is a promising option to bridge and cover these enormous gaps. In this paper, a Smart Collaborative Caching (SCC) scheme is established by leveraging high-level ICN principles for IoT within fog computing paradigm. The proposed solution is supposed to be utilized in resource pooling, content storing, node locating and other related situations. By investigating the available characteristics of ICN, some challenges of such combination are reviewed in depth. The details of building SCC, including basic model and advanced algorithms, are presented based on theoretical analysis and simplified examples. The validation focuses on two typical scenarios: simple status inquiry and complex content sharing. The number of clusters, packet loss probability and other parameters are also considered. The analytical results demonstrate that the performance of our scheme, regarding total packet number and average transmission latency, can outperform that of the original ones. We expect that the SCC will contribute an efficient solution to the related studies.

Dynamic Source Rate Control for Cache-Based Transport Protocol in Wireless Sensor Networks

Reliability of data transport in low power and lossy networks like Wireless Sensor Networks (WSN) is vital due to its constraint characteristics and high probability of packet loss from wireless environment and congestion. Data caching and transmission rate control mechanisms are independent ways of improving the reliability of transport protocols in WSN as they immediately respond to packet losses in the network. However, these two mechanisms are traditionally designed independently and an appropriate rate control that can potentially improve the performance of cache-based transport protocols has not yet been investigated. In this work, a dynamic source rate control algorithm that utilizes a cache-aware approach was developed wherein cache management policies are employed to notify the relative degree of packet losses and limit the transmission window size. A baseline cache-based transport protocol called DTSN+ is used to implement the rate control algorithm and evaluated under network scenarios that experience packet losses from contention and congestion. The dynamic cache-aware algorithm outperformed other transport protocols in terms of cache utilization by 30%. The algorithm also obtained outstanding throughput, transmission time and fairness performance as compared with the fixed DTSN+ and DTC protocols. This demonstrated that a cache-aware approach can improve the performance of a transport protocol during high levels of packet losses in the network. In the future, these results can serve as an underlying support in designing a new cache-aware congestion control framework that is suitable for transport protocols that employ the use of intermediate caching in WSN.

A modified AODV protocol for preventing blackhole attack in MANETs

ABSTRACTWireless Mobile Ad-Hoc Networks (MANETs) are infrastructure-less networks commonly used for dynamic connection between nodes. These networks work without any external router for maintaining the connection and the nodes themselves behave like a router. Ad-Hoc On Demand Distance Vector (AODV) routing is a widely used protocol for managing communications between the mobile nodes. A blackhole attack is one in which the nodes create a false route through them and then perform malicious activities on the transmitted packets. This paper presents a modification of the existing AODV routing protocol to prevent blackhole attacks in MANETs in an erratic terrain with a high probability of packet loss. A mathematical proof is given to confirm the effectiveness of the proposed algorithm with respect to the previous solution in the literature.

Scalable hybrid MAC protocol for M2M communications

Machine-to-machine (M2M) communications is one of the enabling technologies for connecting massive number of devices to the Internet of Things (IoT). M2M communications have different characteristics than human-to-human (H2H) communications. In this work, we propose a scalable, hybrid MAC protocol that will satisfy quality-of-service (QoS) requirements of the users. The proposed MAC protocol organizes transmissions into frames. Each machine is assumed to generate probabilistically one or zero packet per frame. The model allows machines with different packet generation probabilities. This random packet generation enables modeling of both periodic and nonperiodic traffic. The generated traffic has been classified into different classes according to their packet loss tolerances. Each frame has been divided into a number of subframes each serving a class of traffic. Further, each subframe is divided into two subperiods one serving contention and the other reserved traffic of that class. We derived packet loss probability for each class of traffic. Then, we formulated a nonlinear optimization problem (NLP) that minimizes frame length subject to packet loss requirements of different classes. We compare the performance of the proposed protocol with other protocols proposed for M2M communications, which shows that it achieves better performance for small packet sizes.

An Efficient Network Coding-Based Fault-Tolerant Mechanism in WBAN for Smart Healthcare Monitoring Systems

As a key technology in smart healthcare monitoring systems, wireless body area networks (WBANs) can pre-embed sensors and sinks on body surface or inside bodies for collecting different vital signs parameters, such as human Electrocardiograph (ECG), Electroencephalograph (EEG), Electromyogram (EMG), body temperature, blood pressure, blood sugar, blood oxygen, etc. Using real-time online healthcare, patients can be tracked and monitored in normal or emergency conditions at their homes, hospital rooms, and in Intensive Care Units (ICUs). In particular, the reliability and effectiveness of the packets transmission will be directly related to the timely rescue of critically ill patients with life-threatening injuries. However, traditional fault-tolerant schemes either have the deficiency of underutilised resources or react too slowly to failures. In future healthcare systems, the medical Internet of Things (IoT) for real-time monitoring can integrate sensor networks, cloud computing, and big data techniques to address these problems. It can collect and send patient’s vital parameter signal and safety monitoring information to intelligent terminals and enhance transmission reliability and efficiency. Therefore, this paper presents a design in healthcare monitoring systems for a proactive reliable data transmission mechanism with resilience requirements in a many-to-one stream model. This Network Coding-based Fault-tolerant Mechanism (NCFM) first proposes a greedy grouping algorithm to divide the topology into small logical units; it then constructs a spanning tree based on random linear network coding to generate linearly independent coding combinations. Numerical results indicate that this transmission scheme works better than traditional methods in reducing the probability of packet loss, the resource redundant rate, and average delay, and can increase the effective throughput rate.

Cross-Layer Performance of Downlink Dynamic Cell Selection With Random Packet Scheduling and Partial CQI Feedback in Wireless Networks With Cell Sleeping

In this paper, we consider a coordinated multipoint dynamic cell selection (DCS) transmission scheme for serving sleeping cell user equipments (UEs). According to this DCS scheme, packets of UEs in a sleeping cell are randomly forwarded to the potential active base stations (BSs) by the packet serving gateway and UEs in the sleeping cell dynamically select their serving BS from these active BSs. We model the system as a fork/join (F/J) queuing system and develop a cross-layer analytical model that considers the time varying nature of the channels, channel scheduling mechanism, partial channel quality information feedback, cell selection mechanism, bursty packet arrivals, and packet scheduling mechanism. The developed analytical model can be used to measure various packet-level performance parameters, such as packet loss probability (PLP) and queuing delay while accounting for out-of-sequence packet delivery. We validate the accuracy of the developed analytical model via simulations, and we compare the performance of the DCS scheme under consideration with the conventional fixed cell selection scheme and with the state-of-the-art DCS scheme. Presented numerical results show that the DCS scheme under consideration significantly improves the PLP performance. Queuing delay performance, on the other hand, depends on the system and operating parameters.

Fuzzy Logic-based Intelligent Scheme for Enhancing QoS of Vertical Handover Decision in Vehicular Ad-hoc Networks

The design of next generation networks in various technologies under the “Anywhere, Anytime” paradigm offers seamless connectivity across different coverage. A conventional algorithm such as RSSThreshold algorithm, that only uses the received strength signal (RSS) as a metric, will decrease handover performance regarding handover latency, delay, packet loss, and handover failure probability. Moreover, the RSS-based algorithm is only suitable for horizontal handover decision to examine the quality of service (QoS) compared to the vertical handover decision in advanced technologies. In the next generation network, vertical handover can be started based on the user’s convenience or choice rather than connectivity reasons. This study proposes a vertical handover decision algorithm that uses a Fuzzy Logic (FL) algorithm, to increase QoS performance in heterogeneous vehicular ad-hoc networks (VANET). The study uses network simulator 2.29 (NS 2.29) along with the mobility traffic network and generator to implement simulation scenarios and topologies. This helps the simulation to achieve a realistic VANET mobility scenario. The required analysis on the performance of QoS in the vertical handover can thus be conducted. The proposed Fuzzy Logic algorithm shows improvement over the conventional algorithm (RSSThreshold) in the average percentage of handover QoS whereby it achieves 20%, 21% and 13% improvement on handover latency, delay, and packet loss respectively. This is achieved through triggering a process in layer two and three that enhances the handover performance.

On Optimal Spectrum Access of Cognitive Relay With Finite Packet Buffer

We investigate a cognitive radio (CR) system where secondary user (SU) relays primary user (PU) packets using two-phase relaying. SU transmits its own packets with some access probability in relaying phase using time sharing. PU and SU have queues of finite capacity that results in packet loss when the queues are full. Utilizing knowledge of relay queue state, SU aims to maximize its packet throughput while keeping packet loss probability of PU below a threshold. By exploiting structure of the problem, we formulate it as a linear program and find optimal access policy of SU. We also propose low complexity sub-optimal access policies, namely constant probability transmission and step transmission. Numerical results are presented to compare performance of proposed methods and study the effect of queue sizes on packet throughput.

A sub-optimal method for routing redundancy design over lossy networks

In this paper we aim at rendering a wireless networked control system robust with respect to packet losses exploiting routing redundancy. More precisely, we send multiple copies of sensing and actuation data via different routing paths, associated to possibly different delays and packet loss probability, and assume that such data can be re-combined as a weighted linear combination. We extend previous results from the SISO state-feedback to the MIMO output-feedback architecture and provide a methodology to design such weights either to maximize or to constrain a robustness metric based on the notion of Asymptotic Mean Square Stability. Such a metric induces an objective or constraint function that is highly non-linear: we propose a more efficient sub-optimal design methodology that takes into account both the network parameters and the plant dynamics.

Optimization of the Data Transmission Flow from Moving Object to Nonhomogeneous Network of Base Stations

One of the common aims of an Unmanned Aerial Vehicle (UAV) performing an autonomous mission consists in data collection. Often the collected data needs to be transmitted immediately while the UAV is still in the air. This can be done through the net of receiving base stations by means of wireless communication. The problem is that any of the stations chosen for data transmission may be in different and a priori unknown state. Moreover, the transmission channel with any base station is variable due to the weather conditions, varying distance or obstacles between the station and the UAV. This randomly changing environment makes the selection of a station to communicate rather important, so the optimal data transmission problem should involve an auxiliary problem of the transmission channel state (TCS) estimation. In order to estimate the TCS one can use the information available to the on-board transmitter: since the probability of packet loss depends on the channel state, counting the number of lost packets may provide indirect measurements of the state. Thus the only resource for the solution of both the estimation and the optimal control problems becomes the transmitted packets, whose number is restricted by the transmitter capabilities and the UAV power source. In this work we give a statement of this new class of simultaneous estimation and stochastic control problems and provide the solution in a feedback form.

H∞ - Optimal Control over erasure channel

In this paper, the optimal H∞ control of a linear time invariant (LTI) networked control system (NCS) over a lossy network is analyzed. Specifically we consider a TCP-like data network where packet drops are modelled using a two-state Markov process. By employing the theory of dynamic games, a state-feedback H∞ controller is obtained. It is shown that, subjected to a suitable H∞ disturbance attenuation level and suitable packet loss probabilities, such a controller exists for both finite and infinite horizon cases. We also show the asymptotic stability of the NCS in the mean-square sense. Finally a numerical example is presented to demonstrate our work.

Providing traffic tolerance in optical packet switching networks: a reinforcement learning approach

The servitization of network resources leads to new challenges for optical networks. For instance, to provide on-demand lightpaths as a service while keeping the probability of packet loss (PPL) low, issues such as lightpath setting up, resource reservation and load balancing must be addressed. We present a self-adaptive framework to process lightpath requests on packet switching optical networks that considers and handles the aforementioned issues. The framework is composed of a dimensioning phase that adds up new resources to an initial topology and a learning phase based on reinforcement learning that provides self-adaptation to tolerate traffic changes. The framework is tested on three realistic mesh topologies achieving a PPL between $$1 \times 10^{-1}$$ and $$1 \times 10^{-6}$$ for different traffic loads. Compared to fixed multi-path routing strategies, our framework reduces PPL between $$19\%$$ and up to $$80\%$$ . Furthermore, no packet loss can also be achieved for traffic loads equal to or lower than 0.4.

Stabilization of uncertain systems using quantized and lossy observations and uncertain control inputs

In this paper, we consider a stabilization problem of an uncertain system in a networked control setting. Due to the network, the measurements are quantized to finite-bit signals and may be randomly lost in the communication. We study uncertain autoregressive systems whose state and input parameters vary within given intervals. We derive conditions for making the plant output to be mean square stable, characterizing limitations on data rate, packet loss probabilities, and magnitudes of uncertainty. It is shown that a specific class of nonuniform quantizers can achieve stability with a lower data rate compared with the common uniform one.

An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced

Long term evolution-advanced (LTE-A) system introduces carrier aggregation (CA) technique to improve the user throughput by aggregating multiple component carriers (CCs). Previous research works related to downlink radio resource allocation with carrier aggregation have not considered the delay factor and the error probability. Therefore, the previous methods are unable to provide better quality of service (QoS) compared to the LTE-A standard. This paper considers the radio resource management problem by zooming into the head of line delay, probability of packet loss, and the delay threshold for different types of data. In doing this, several constraints are imposed following the specifications of LTE-A system. Hence, an improved method is developed in this study to enhance the system throughput and to maintain the computational complexity. Extensive simulations were carried out with other well-known methods to verify the overall performance of the proposed method. The result obtained indicates that the proposed method outperforms the previous methods in the measurement of average user throughput, average cell throughput, fairness index, and spectral efficiency.

Performance of Gaussian and Non-Gaussian Synthetic Traffic on Networks-on-Chip

In this paper, we have worked on the bursty synthetic traffic for Gaussian and Non-Gaussian traffic traces on the NoC architecture. This is the first study on the performance of Gaussian and Non-Gaussian application traffic on the multicore architectures. The real-time traffic having the marginal distribution are Non-Gaussian in nature, so any analytical studies or simulations will not be accurate, and does not capture the true characteristics of application traffic. Simulation is performed on synthetic generated traces for Gaussian and Non-Gaussian traffic for different traffic patterns. The performance of the two traffics is validated by simulating the parameters of packet loss-probability, average link-utilization & average end-to-end latency shows that the Non-Gaussian traffic captures the burstiness more effectively as compared to the Gaussian traffic for the desired application.

Capacity and spectrum-aware communication framework for wireless sensor network-based smart grid applications

Recently, wireless sensor networks (WSNs) have been widely recognized as a promising technology for enhancing various aspects of smart grid and realizing the vision of next-generation electric power system in a cost- effective and efficient manner. However, recent field tests show that wireless links in smart grid environments have higher packet error rates and variable link capacity because of dynamic topology changes, obstructions, electromagnetic interference, equipment noise, multipath effects, and fading. To overcome these communication challenges, in this paper, we propose a data capacity-aware channel assignment (DCA) and fish bone routing (FBR) algorithm for WSN-based smart grid applications. The proposed DCA framework deals with the channel scarcities by dynamically switching between different spectrum bands and employs a network for organizing WSN into a highly stable connected hierarchy. In addition, the proposed FBR mechanism provides robust loop free data paths and avoids high transmission cost, excessive end-to-end delay and restricts unnecessary multi-hop data transmission from the source to destination in the network. Thus, it significantly reduces the probability of data packet loss and preserves stable link qualities among sensor nodes for load balancing and prolonging the lifetime of wireless sensor networks in harsh smart grid environments. Comparative performance evaluations show that our proposed schemes outperform the existing communication architectures in terms of data packet delivery, communication delay and energy consumption.

Transmission Policy Design for Mobile Nodes with Wireless Energy Harvesting in Cellular Networks

In this paper, we mainly focus on transmission policy design for the mobile nodes in wireless cellular networks in which the mobile nodes are with the wireless energy harvest capability. In particular, we consider the problem how to find a good policy under the minimum throughput demand and maximum delay constraint of data transmission. To do so, the optimal transmission policy is selected as minimizing the corresponding packet loss probability while satisfying the basic requirements on throughput and delay. We first characterize the system model by Constrained Markov Decision Process (CMDP) and formulate the optimal problem and present the performance calculation methods by using the similar method in [5]. The key difference is that we assume mobile user can always harvest energy when moving in the coverage area of one power transmit station and the power transmit stations are working in a probability mode rather than by observing the users number in their own coverage area. Various simulation results are presented and shown that our designed optimal policy has better performances than two conventional policies. We also find that increasing the battery power storage capacity will increase the real achieved throughput and reduce the packet loss probability under certain conditions. In addition, we also find that if the minimum through put demand is larger than a value, its real achieved throughput and the packet loss probability will not clearly change as the battery capacity are large enough.