Search

Industrial Internet of Things and Industry 4.0 enable interconnection among various devices. An opportunistic Internet of Things network is an ad-hoc network that is formed by the nodes (e.g. smart vehicles and mobile phones) by utilizing various short radio range techniques. In this kind of network, information forwarding and dissemination among other smart devices is based upon the opportunistic contact nature mainly due to network dynamics and user mobility. Routing plays an important part in these kinds of networks since there does not exist a pre-established route tyagi2013systematic. Nodes are often selected dynamically based upon many parameters such that messages can be delivered successfully to the destination devices or sinks. However, these intermediate nodes are often selfish because routing these packets costs energy. In case of incomplete cooperation and asymmetric information, message delivery can be severely degraded, which increases the network delay affecting the overall network performance. Therefore, this work proposes an incentive design mechanism based on contract theory to reward intermediate nodes appropriately to forward the messages. Contract theory is used to model the forwarding-forwarder node interaction as a labor market with private information. First, the users are classified into a finite number of types according to their ability of forwarding the message, and the service trading between the forwarding and forwarder nodes is properly modeled. Further, the necessary and sufficient conditions are derived to provide the incentives to the nodes involved in the message forwarding. Extensive simulations show that the proposed mechanism is effective in providing incentives and outperforms other benchmark schemes in terms of delivery probability, average latency, and overhead ratio.

The Adaptive Data Rate (ADR) algorithm is used in LoRaWAN, allocating an appropriate transmission rate for terminal equipment to improve channel utilization and reduce power consumption. However, the standard ADR algorithm is only suitable for static terminal equipment. In addition, due to the complexity of the external environment, the selected data rate will not match the new environment and communication packets will be lost. Therefore, this paper proposed a novel and more effective ADR algorithm called New-Dynamic ADR (ND-ADR). The algorithm mainly solves two problems, i.e., the standard ADR algorithm cannot be applied to mobile terminal devices and the poor communication quality and high packet loss rate in harsh environments. In this study, we also developed FLoRaWAN (Frame for LoRaWAN), a simulation framework for the star network topology LoRaWAN in OPNET. Furthermore, we built an OKUMURA-HATA model and additionally introduced a noise factor β to simulate the loss of wireless communication in harsh environments. Finally, extensive simulation results showed that the number of data packets required by different end nodes for rate allocation was different. Compared with the standard ADR algorithm, even for mobile nodes in harsh environments, the ND-ADR algorithm reduced network energy consumption by about 13%, reduced network delay by about 18%, and increased effective throughput by about 15%. Therefore, the improved ND-ADR algorithm is more suitable for wireless communication of removable nodes in harsh environments. Its advantages are better awareness of link environment, faster data rate regulation, improved channel utilization, and further reduction of network energy consumption.

An ad hoc network is a network made of movable nodes built on the fly and constructed dynamically when there is no longer an infrastructure or pre-built network on the scene. The nodes can also perform both end-to-end terminals and shape MANET roles, meaning they contribute to each route's configurations. The data must travel through several intermediate nodes during the route while routing a given flow from a source to a destination. The data should be larger than needed for just a single application use. The intermediate nodes forward data to the intended destination, taking into account the locations to which it must be sent to arrive correctly. The routing protocol in an Ad hoc network (human-made network) uses nodes to look at different device locations and procedures for any data path they choose before deciding on the route and ways in which they exchange data are viable. Anomaly, since the existing trust among the various nodes and dynamic topology, leaves the routing protocols susceptible to Denial of service attacks like a black hole, wormhole, Denial of service, and being non-integrated into a central infrastructure present a condition which MANETs are subject to such as Conventional networks typically work by injecting some control packets and tracking the target's movement once the user is already on the wireless. Still, in a MANET, the attacker acts before the mark has moved into the wireless medium. Maliciously exploiting various routing information has the potential to drive the system into a situation of more significant disorder, which will eventually lead to widespread network failure. This existing AODV attack, called the Blackhole attack, might have worked by purposefully withholding important routing information from end-users who could have benefitted from it, which is just what the adversary does in this task. In this scenario, the data packets are never delivered, and the system suffers a total data loss. The variety of detection and protection techniques employed against the blackhole attacker significantly lower the number of suspects. Therefore, this paper advocate for OSPFV [for wireless LANs] protocol integration with built-in security, where Threshold evaluation and cryptographic verification are employed. In this paper, two protocols: the blackhole attack and the proposed AODV-BS protocols, are simulated on different MANET models, and the two other network metrics are used: Network Packet Delivery Ratio and the normalized Out of Routing Overhead Utilization and Network Delay are calculated, and then their performance is studied to discover the result.

In IEEE 802.15.4 standard for low-power low-range wireless communications, only one channel is employed for transmission which can result in increased energy consumption, high network delay and poor packet delivery ratio (PDR). In the subsequent IEEE 802.15.4-2015 standard, a Time-slotted Channel Hopping (TSCH) mechanism has been developed which allows for a periodic yet fixed frequency hopping pattern over 16 different channels. Unfortunately, however, most of these channels are susceptible to high-power coexisting Wi-Fi signal interference and to possibly some other ISM-band transmissions. This interference manifests itself in the form of the presence/absence of other devices with either or both static and dynamic channel selection policies. In order to isolate channels with undesirable conditions, blacklisting mechanisms are defined to adapt the channel hopping process. However, the existing solutions which form blacklists unrealistically assume that the statistical model of the external interference remains fixed, and do not vary over time. In this paper, we realistically assume that the impact of external interferes on 802.15.4 may generally follow a non-stationary pattern, and accordingly formulate the adaptive channel hopping problem as a Dynamic Multi-Armed Bernoulli Bandit (Dynamic MABB) process from the machine learning theory. We then propose an online learning algorithm with track-ability properties for computing an adaptive hopping policy. Simulations confirm that when the statistics of the external interference has a switching regime, the proposed solution outperforms the previous schemes in terms of both energy efficiency as well as two important KPIs for TSCH-based networks, i.e., PDR and latency.

Purpose The purpose of this study is to calculate the effect of different packet sizes 256, 512, 1,024 and 2,048 bytes on a large-scale hybrid network and analysis and identifies which routing protocol is best for application throughput, application delay and network link parameters for different packet sizes. As the routing protocol is used to select the optimal path to transfer data packets from source to destination. It is always important to consider the performance of the routing protocol before the final network configuration. From the literature, it has been observed that RIP (Routing Information Protocol) and OSPF (Open Shortest Path First) are the most popular routing protocols, and it has always been a challenge to select between these routing protocols, especially for hybrid networks. The efficiency of routing protocol mainly depends on resulting throughput and delay. Also, it has been observed that data packet size also plays an essential role in determining the efficiency of routing protocol. Design/methodology/approach To analyse the effect of different packet sizes using two routing protocols, routing information protocol (RIP) and open shortest path first (OSPF) on the hybrid network, require detailed planning. Designing the network for simulate and then finally analysing the results requires proper study. Each stage needs to be understood well for work accomplishment. Thus, the network’s simulation and evaluation require implementing the proposed work step by step, saving time and cost. Here, the proposed work methodology is defined in six steps or stages. Findings The simulation results show that both routing protocols – RIP and OSPF are equally good in terms of network throughput for all different packet sizes. However, OSPF performs better in terms of network delay than RIP routing protocol in different packet size scenarios. Research limitations/implications In this paper, a fixed network of 125 objects and only RIP and OSPF routing protocol have been used for analysis. Therefore, in the future, a comparison of different network sizes can be considered by increasing or decreasing the number of objects in the proposed network. Furthermore, the other routing protocols can be used for performance evaluation on the same proposed network. Originality/value The analysis can be conducted by simulation of the network, enabling us to develop a network environment without restricting the selection of parameters as it minimizes cost, network deployment overhead, human resources, etc. The results are analysed, calculated and compared for each packet size on different routing protocol networks individually and the conclusion is made.

This article focuses on the problem of stabilizing a scalar continuous-time nonlinear system under bounded network delay and process noise. In order to save the feedback network’s bandwidth, a model-based periodic event-triggered control policy is utilized to maintain longer intersampling intervals, which are at least as long as the sampling period of periodic policies. Furthermore, the event-triggering condition is only checked intermittently at fixed time instants, i.e., the sampling time instants. Without acknowledgment (ACK), the updating of nominal models at the sensor and at the controller are asynchronous. The two cases, where the network delay is either less than the sampling period or larger than the sampling period, are investigated. In comparison with periodic sampling methods, our scheme can make full use of the received data packets, particularly their sampling time instant information, which yields a lower occupied bit rate while guaranteeing the desired input-to-state stability. Note that the obtained bit rate conditions are only related to the Lipschitz parameter, the bound of network delay, the number of quantization bits, and the sampling period. The bounded process noise will not incur any increase of the stabilizing bit rate under the proposed strategy.

Maintaining the network QoS is one of the critical challenges emerging in heterogeneous WBANs. High throughput, minimum delay, and maximum lifetime are the critical network performance metrics pre-requisites for achieving the network QoS. The use of Software-Defined Networking (SDN) concept has been recently adopted in dynamic and complex structured WBANs due to the existence of wide network management flexibility. Leveraging the benefits of SDN in cognitive radio (CR) based architectures, we have presented ERQTM: Energy-efficient Routing and QoS-supported Traffic Management scheme for SDWBANs. The main contributions of the proposed scheme are enhancing the network lifetime and achieving the network stability and reliability. Thus, the problem is addressed through a joint scheme, developing an energy-efficient routing scheme (ER) and traffic management scheme. The proposed ERQTM framework is fundamentally based on network clustering which proves to have positive impact on overall network delay. As clustering is an optimizing and an NP-Hard problem, this paper adopts genetic algorithm to optimally find the required cluster heads (CHs) in the ER scheme taking into consideration the nodes-related matters: residual energy, energy consumption rate during consecutive intervals, distance to the SDN controller, signal to noise ratio, and the path loss model. Besides clustering, prioritizing the emergency data (ED) in the traffic management scheme is presented that efficiently handles the packets by ensuring free-delay for ED transmission. This is done by assigning high priority for routing, high data rate, and needed-related power. Performance evaluation results showed that the proposed ERQTM scheme has outperformed different priority-based state-of-the-art schemes.

The rapid development of Internet of Things (IoT) can promote the establishment of the smart connected world by using numerous devices and sensors. Collecting the data perceived by sensing devices in a fast and energy-saving style is critical for building a stable network in IoT. In this paper, we propose an Early Message Ahead Join Adaptive Data Aggregation (E-ADA) scheme for IoT. Firstly, an advance notification mechanism based on early message is introduced to improve the probability of data aggregation, thus optimizing energy consumption and network lifetime. In this mechanism, the routing of early message is faster than that of the data packet. Nodes with data packet send an early message forward to notice, and other data packets which monitor the early message can wait for that to aggregate under the delay deadline constraints. Secondly, we propose a Delay-optimized Convergence Routing based E-ADA (DOCR-E-ADA) data collection scheme which combines advance notification mechanism and convergence routings. The duty cycle of nodes in convergence routing is adaptively adjusted, which greatly reduces transmission latency. Finally, extensive experimental results demonstrate that our DOCR-E-ADA outperforms the existing schemes in terms of network delay and lifetime.

The efficient antenna scheduling strategy for data relay satellites (DRSs) is essential to optimize the throughput or delay of the satellite data relay network. However, these two objectives conflict with each other since the user satellites (USs) with higher priorities take up more transmission time of DRSs' antennas for greater throughput but the USs storing more packets cause a severer waiting delay to the whole network. To balance the conflicting metrics for meeting the delay-throughput integrated requirements, we formulate the antenna scheduling as a stochastic non-convex fractional programming, which is challenging to be solved. For the tractability, we equivalently transform the fractional programming to a parametric problem and implement the Lyapunov drift to guarantee the constraint of mean rate stability. By proposing a delay and throughput tradeoff based antenna scheduling algorithm, we further transform the parametric problem to a solvable weight matching problem. Simulation results reveal the feasible region of the preference control parameter for integrated QoS cases and its variation relationship with network delay and throughput.

This paper investigates the problem of stabilizing an n -dimensional nonlinear system whose feedback packets are transmitted via the digital network. In the presence of the bounded processing delay and network delay, the sampling time instant of a feedback packet is not the same as its receiving time instant. The larger difference between these time instants, the higher bit rate is required to stabilize the concerned system. Moreover, not only these delays, but also feedback dropouts and process noise will deteriorate the system performance. Thus we propose a model-based event-triggered control method to stabilize the system and save the occupied network bandwidth. Besides the transmission bits inside the feedback packets, their sampling time instants also convey the state information. The proposed event-triggered method can make full use of the feedback packets, especially their receiving time instant information. Compared with the periodic sampling method, our method performs better and requires lower stabilizing bit rates while the desired input-to-state stability can still be ensured. The obtained bit rate conditions only depend on the Lipschitz constant, the upper bounds of the delays and the dropout rate, and are independent of the bounded process noise.

Networked Music Performance (NMP) is envisioned as a potential game changer among Internet applications: it aims at revolutionizing the traditional concept of musical interaction by enabling remote musicians to interact and perform together through a telecommunication network. Ensuring realistic conditions for music performance, however, constitutes a significant engineering challenge due to extremely strict requirements in terms of audio quality and, most importantly, network delay. To minimize the end-to-end delay experienced by the musicians, typical implementations of NMP applications use un-compressed, bidirectional audio streams and leverage UDP as transport protocol. Being connection less and unreliable,audio packets transmitted via UDP which become lost in transit are not re-transmitted and thus cause glitches in the receiver audio playout. This article describes a technique for predicting lost packet content in real-time using a deep learning approach. The ability of concealing errors in real time can help mitigate audio impairments caused by packet losses, thus improving the quality of audio playout in real-world scenarios., 8 pages, 2 figures

5G communication technology has been applied to several fields in telemedicine, but its effectiveness, safety, and stability in remote laparoscopic telesurgery have not been established. Here, we conducted four ultra-remote laparoscopic surgeries on a swine model under the 5G network. The aim of the study was to investigate the effectiveness, safety, and stability of the 5G network in remote laparoscopic telesurgery. Four ultra-remote laparoscopic surgeries (network communication distance of nearly 3000 km), including left nephrectomy, partial hepatectomy, cholecystectomy, and cystectomy, were performed on a swine model with a 5G wireless network connection using a domestically produced “MicroHand” surgical robot. The average network delay, operative time, blood loss, and intraoperative complications were recorded. Four laparoscopic telesurgeries were safely performed through a 5G network, with an average network delay of 264 ms (including a mean round-trip transporting delay of 114 ms and a 1.20% data packet loss ratio). The total operation time was 2 h. The total blood loss was 25 ml, and no complications occurred during the procedures. Ultra-remote laparoscopic surgery can be performed safely and smoothly with 5G wireless network connection using domestically produced equipment. More importantly, our model can provide insights for promoting the future development of telesurgery, especially in areas where Internet cables are difficult to lay or cannot be laid.

Reactive jamming attack, enabled by programmable software defined radio, has become a serious threat to the ubiquitous wireless systems. Wireless scheduling under reactive jamming attack is important yet challenging. In this article, we consider the utility optimal scheduling problem for multi-hop wireless networks under reactive jamming attack. We propose pilot-aided channel probing , defensive power allocations, and jamming-aware route-selection mechanisms to counteract reactive jamming. By incorporating the link-level packet delivery ratio (PDR), we devise effective online learning approaches to improve network performance over time. The constantly sought optimal scheduling policy on the conflict graph is found to be between the well-known “experts” setting and the “Multi-armed bandits (MAB)” setting. More importantly, the PDRs treated as “expert advices” (contextual information) to recommend optimal schedules online over time makes the scheduling an intrinsic combinatorial contextual MAB (CMAB) problem. The utility optimal scheduling problem is formulated as a stochastic optimization framework to minimize the incurred regret. We define two rigorous reactive jamming attack models. An anti-jamming protocol is then proposed with provable utility and regret guarantees. We conduct both synthetic and real experiments to validate our theory. Experimental results show that our algorithm achieves 35%∼97% and 79%∼138% improvements of network delay and 23%∼58% and 67%∼108% improvement of network throughput over other state-of-the-art approaches for oblivious jamming and adaptive jamming, respectively. These results indicate that our framework could sustain high throughput under powerful reactive jamming attacks.

In this paper, we investigate the capacity and delay tradeoff under multicast scheme in the MANET, based on a general Markovian mobility model. To reduce the delay of the network, we propose a relay-assisted multicast scheme. Specifically, a two-hop relay algorithm is developed, in which Lyapunov drift is utilized to derive the average packet delay. In addition, we utilize the cache in relay nodes and propose the two-hop relay algorithm with redundancy. Theoretical analysis indicates that the network delay is significantly decreased, whereas the capacity remains the same. To guarantee the fairness and efficiency of the network, a two-hop relay selection algorithm with redundancy is proposed to decide which packet to serve in a queue. Moreover, the minimum energy function is applied to characterize the energy consumption of each node. We accordingly derive the accurate minimum energy function under the proposed relay-assisted multicast scheme. Furthermore, we design an efficient minimum energy algorithm, which pushes the actual energy consumption arbitrarily close to the minimum energy function at the cost of increasing delay. Theoretical results show that the optimal energy-delay tradeoff is achieved in our proposed algorithm. Numerous experiments are carried out to evaluate the performance of our proposed algorithms, where the experimental results well conform our theoretical findings.

This paper focuses on a scalar nonlinear system, whose feedback packets are transmitted through an unreliable digital communication network. The network is band-limited and suffers from external disturbance, bounded transmission delay and i.i.d. feedback dropouts. To save communication resource, a periodic event-triggered strategy is proposed to maintain the mean square stability of the concerned system. In comparison with periodic sampling, our strategy can stabilize the system at a lower bit rate. Note that the obtained bit rate condition is up to the Lipschitz parameter, the bound of network delay, the dropout rate, the sampling period and the number of quantization bits. Some simulations are provided to verify the effectiveness of our proposed method.

This paper considers to stabilize an uncertain scalar continuous-time nonlinear system with bounded network delay and process noise, which transmits all feedback signals through a digital communication network. In order to save the bandwidth of the feedback network, stability is expected to be maintained at as low as possible feedback bit rate. Based on event triggering, this paper proposes a model-based event-triggered sampling strategy to guarantee the desired input-to-state stability of the concerned system. Due to the bounded network delay, the receiving time instant of a feedback packet cannot be precisely controlled by the sensor, i.e., the receiving time instant is not always equal to its sampling time instant. Their gap, i.e., the network delay, determines how much information can be carried through the receiving time instant and makes great impact on the system’s stability. Sufficient bit rate conditions to stabilize that system are derived. The conditions are determined by the parameter of Lipschitz condition, the upper bound of the network delay and the system uncertainty. Compared with the periodic sampling strategies, a lower bit rate is required by the proposed event-triggered strategy. Simulations are done to verify the effectiveness of the achieved stabilizing bit rate conditions.

Many clock synchronization protocols based on message passing, e.g., the Network Time Protocol (NTP), assume symmetric network delays to estimate the one-way packet transmission time as half of the round-Trip time. As a result, asymmetric network delays caused by either network congestion or malicious packet delays can cause significant synchronization errors. This article exploits sinusoidal voltage signals of an alternating current (AC) power grid to limit the impact of the asymmetric network delays on these clock synchronization protocols. Our extensive measurements show that the voltage signals at geographically distributed locations in a city are highly synchronized. Leveraging calibrated voltage phases, we develop a new clock synchronization protocol that we call Grid Time Protocol (GTP), which allows direct measurement of one-way packet transmission times between its slave and master nodes, subject to an analytic condition that can be easily verified in practice. The direct measurements render GTP resilient against asymmetric network delays under this condition. A prototype implementation of GTP maintains sub-millisecond synchronization accuracy for two nodes tens of kilometers apart in the presence of malicious packet delays. The result has been demonstrated for both Singapore and Hangzhou, China. Simulations driven by real network delay measurements between Singapore and Hangzhou under both normal and congested network conditions also show the synchronization accuracy improvement by GTP. We believe that GTP is suitable for grid-connected distributed systems that are currently served by NTP but desire higher resilience against unfavorable network dynamics and packet delay attacks. Accepted version

The emergence of Network Function Virtualization (NFV) enabled decoupling network functionality from dedicated hardware and placing them upon generic computing resources. Moreover, the introduction of edge computing paradigm which utilized the resources at the network edges brings reduced end-to-end latency. With these technologies, Virtualized Network Functions (VNFs) can be placed in anywhere either in the central clouds to utilize more resources or in the network edges to reduce the end-to-end latency. In this work, we propose a dynamic resource provisioning algorithm for VNFs to utilize both edge and cloud resources. Adapting to dynamically changing network volumes, the algorithm automatically allocates resources in both the edge and the cloud for VNFs. The algorithm considers the latency requirement of different applications in the service function chain, which allows the latency-sensitive applications to reduce the end-to-end network delay by utilizing edge resources over the cloud. We evaluate our algorithm in the simulation environment with large-scale web application workloads and compare with the state-of-the-art baseline algorithm. The result shows that the proposed algorithm reduces the end-to-end response time by processing 77.9% more packets in the edge nodes compared to the application non-aware algorithm.

The Fifth Generation (5G) mobile network will revolutionize the way of communication by supporting new innovative applications that require low latency and high data rates in smart city environments. In order to meet these applications' requirements, Ultra-Dense Network (UDN) is considered as one of the promising technological enablers in 5G. 5G UDN deployments are envisaged to be heterogeneous and dense, mainly through the provisioning of small cells such as picocells and femtocells, from different Radio Access Technologies (RATs). Nevertheless, various studies have reported that the densification is not always beneficial to the network performance. As the network density increases, this will pose further requirements and complexity of determining which RAT a user should connect with at a given time. Hence, an efficient RAT selection mechanism to choose the best Radio Access Technology among multiple available ones is a must. This paper proposes a new Context-aware Radio Access Technology (CRAT) selection mechanism that examines the context of the user and the networks in choosing the appropriate RAT to serve. A simplified conceptual model of the Context-aware RAT selection is introduced. Then, a mathematical model of CRAT considering the user and network context is derived, adopting Analytical Hierarchical Process (AHP) for weighting the importance of the selection criteria and TOPSIS for ranking the available RATs. The proposed CRAT was implemented and validated in NS3 simulation environment. The performance of the proposed mechanism was tested using two different scenarios within a smart city environment, called a shopping mall and urban city scenarios. The obtained results showed that CRAT outperforms the conventional approach namely A2A4 of RAT selection in terms of the number of handovers, average network delay, throughput, and packet delivery ratio.

Due to the limit of computation and storage capability of devices, traditional security techniques face many challenges in the process of data transmission. Networks need to consume more energy and larger transmission for transmitting data in insecure network status. However, it is of great importance to deliver packets to the base station (or sink node) in a timely manner to support the delay-sensitive applications. For Wireless Sensor Networks (WSNs), it is challenging to satisfy end-to-end delay requirements due to the duty cycle adopted by the nodes, which can cause considerable delay because nodes can only transmit or receive information in their active periods (i.e., resulting in sleep delay). To address this issue, a dynamic duty cycle (DDC) scheme is proposed for minimizing the delay in WSNs. Specifically, we firstly investigate how the duty cycle affects the network delay. Then, the DDC scheme is devised to prolong the active period of nodes in non-hotspots areas. With a greater duty cycle, the forwarding node set remains awake with a larger chance. Consequently, the sleep delay of a node decreases, and the transmission delay is reduced. In addition, only the remaining energy of nodes is used to improve the performance. Thus, the DDC scheme does not damage network lifetime. Through analysis and experimental results, it is demonstrated that the DDC scheme can outperform other schemes. Compared with the existing fixed duty cycle (SDC) scheme, the transmission delay in the DDC scheme is reduced by 20–50%, the lifetime is increased by more than 16.7%, and the energy efficiency is improved by 15.3%–16.3%.

The importance of remotely placed controller in a cyber space with sensor–controller–actuator network has increased significantly in industrial, defense, and surveillance sector. Such network has large amount of sensor and controller data. A time-triggered control technique may generate redundant control signals and put unnecessary data on network. Therefore, an event-triggered adaptive controller that generates control action at required instants using state- and error-based conditions has been developed in this paper. A data transmission framework has also been designed in this paper that addresses network delay and packet losses. The proposed controller-communication methodology has been validated through two case studies, first, temperature tracking for heating ventilation and air conditioning system and, second, real-time path tracking by automated guided vehicle. The proposed methodology has also been duly compared with its time-triggered counterpart. The control updates are reduced to approximately ${\text{41}}\%$ and ${\text{64}}\%$ in the two case studies, respectively. The experimental results also prove the designed controller to be efficient when compared with event-triggered incremental PID controller using the same data transmission framework.

Purpose Application environments of wireless sensor networks (WSNs) include heterogeneous nodes with different packet sizes, transmission abilities and tolerable delay times. This study aims to design a reasonable network topology and transmission timing for these heterogeneous nodes to improve the quality of service (QoS) of networks. Design/methodology/approach In this paper, the authors treat node urgency and data packets as the basis of network clustering and to extend the network lifetime. The flow, energy consumption and residual energy of a node are included in the cluster head election. We also propose a delay evaluation function. Findings All the nodes in the network are guaranteed to transmit to the sink nodes efficiently by planning the transmission order in each cluster. Originality/value The simulation results show that the proposed method can balance node urgency and data packets path planning, which not only extends the lifetime of the network but also decreases network delay and improves the overall efficiency.

Climate-based agriculture is an essential proficiency that maximizes the context based agricultural yields via suitable ambient monitoring services modified with the information gained from the sensors. Monitoring weather in real time scenario is the primary criterion to observe the climate ambient of a farm. Several farms relevant troubles can be resolved by better realizing the ambient weather situation. Increasing agricultural productivity technique is proposed to improve the precision farming field. However, it creates an additional delay in transferring the information to farmer also it creates additional energy consumption. To overcome these problems, we propose ambient crop field monitoring for improving Context based agricultural by mobile sink in wireless sensor networks. Ambient monitoring objective is to increase the yield of crops while diminishing the use of the property. The mobile sink is introduced to collect the updated ambient information from the sensors and send it to the base station. This research work aims to design a better path using a sensor node to a mobile sink and mobile sink travelling path for reducing energy consumption and delay in the context based agricultural wireless sensor networks. In this scheme, the sensor nodes have formed the route based on mobile sink tree. But, it does not reduce the network delay better. To solve this issue, frontward communication area (FCA) based route selection is proposed. The FCA method reduces both the energy consumption and delay in the network since it selects the route by the quality of service parameters. This technique application is mainly used to smart agriculture. The simulation results show that the getting better 38.31% packet received rate and reducing 0.0115 J energy consumption in the network.

This technical note proposes an event-triggered quantization policy design method to stabilize a scalar continuous-time linear time-invariant (LTI) system, whose feedback loop is closed over an unreliable digital communication network. The feedback packets suffer from network delay and may be dropped in an independent and identically distributed (i.i.d.) way. In order to save quantization bit rate, we implement a periodic event-triggering strategy which periodically samples the state and only quantizes and transmits the sampled state when a predefined event is triggered. By making good use of both the information bits and the sampling time instants of the feedback packets, our policy can obtain the desired mean-square stability at a lower bit rate than the conventional time-triggered quantization policies which utilize only the information bits inside the feedback packets. Under our quantization policy, the stabilizing bit rate condition is up to the dropout rate, the processing and network delays and the unstable eigenvalue of the system, and is independent of the bounded process noise.

The usage of sensors and actuators in industrial applications has increased widely. Cyber Physical Systems (CPS) has improved the usage of closed loop control systems using sensors and actuators. However the health of sensors & actuators and their impact on the efficiency of the industry is still a challenging area. The other important aspect is the network related issues that rise due to the large scale of sensors & actuators. There need an efficient mechanism that manages the network delay and packet drops associated with the large scale sensor deployments. There are machine learning techniques which are used to predict the sensor values when it is absent due to various reasons. Similarly, there are anomaly detection techniques that are used to detect faulty sensors. Both the prediction approaches fail to address the properties related to lifetime and service needs of sensors & actuators. This paper proposes a machine learning framework that combines the results of individual learning modules for achieving collective decisions. This proposed method provides the solution to address the delays and failures occurring in large scale deployments.

Vehicular networks use roadside units (RSUs) to increase vehicles' communication capability to relay tracking messages and/or provide vehicle, driver or passenger Internet access. Unfortunately, wireless signal communication inside vehicle networks is primarily influenced by buildings as well as other barriers (e.g. urban installations) In addition, some applications, especially security apps, require fast and effective transmission of alert data to emergency services as well as traffic authorities. However, communicating in car conditions is not always feasible, even with several hops, because there is no connection. An efficient, creative, cost effective and all-purpose RSU implementation policies should be introduced in order to overcome the problem of signal propagation as well as delayed recognize time problems. In this document we suggest the Hybrid Roadside Unit Deployment (HRSUD) framework, which incorporates fuzzy possibility c- means as well as genetic algorithms. The network coverage is provided by a fuzzy possibility c- means (FPCM) clustering. The genetic algorithm is able to provide an RSU deployment automatically suitable for any particular road map configuration. Our simulation results show that the HRSUD introduced decreases alert response cost and enhancing the ability to communicate in a number of intensity and difficulty scenarios and the time to notify emergency security services in dangerous situations. There are works on movement of units, distance measure, transmission time. From the simulation we get optimum results in terms of throughput, packet delivery ratio, and network delay and packet drop rate.

This article aims to stabilize an n -dimensional linear time-invariant (LTI) system, whose feedback packets are transmitted through a digital communication network. The digital network suffers from network delay and independent and identically distributed (i.i.d.) feedback dropouts, which may destabilize the system. The coupling among multiple state variables may further harm the stability of the system. In order to deal with these issues and save the occupied bandwidth of the feedback network, we propose a periodic event-triggering strategy. In our strategy, the state is measured periodically, but only quantized and transmitted when a certain condition is triggered. By well balancing the state coupling and making full use of both the information inside transmitted feedback packets and the one carried by sampling time instants, our strategy can maintain the desired mean square stability at a lower bit rate than conventional periodic sampling policies. The obtained stabilizing bit rate conditions are determined by the processing and network delays, the dropout rate, and the unstable eigenvalues of the system matrix, but independent of the process noise. Moreover, the lack of the direct state access does not incur any additional stabilizing bit rate. Simulations are done to confirm the effectiveness of the obtained stabilizing bit rate conditions.

Aiming at solving network delay caused by large chunks of data in industrial Internet of Things, a data compression algorithm based on edge computing is creatively put forward in this paper. The data collected by sensors need to be handled in advance and are then processed by different single packet quantity K and error threshold e for multiple groups of comparative experiments, which greatly reduces the amount of data transmission under the premise of ensuring the instantaneity and effectiveness of data. On the basis of compression processing, an outlier detection algorithm based on isolated forest is proposed, which can accurately identify the anomaly caused by gradual change and sudden change and control and adjust the action of equipment, in order to meet the control requirement. As is shown by experimental simulation, the isolated forest algorithm based on partition outperforms box graph and K-means clustering algorithm based on distance in anomaly detection, which verifies the feasibility and advantages of the former in data compression and detection accuracy.

A fragment of a Software-Defined Networking (SDN) architecture with the functionality of switches and controllers is described as the mathematical model of a multiphase queueing system with losses at each phase. In the IETF draft “Address Resolution Delay in SDN” (also, see ITU-R M.2083-0), it was recommended to reduce network delay within a single SDN segment from 50 ms to 1 ms. This performance index considerably depends on the characteristics of the software and hardware system of SDN networks as well as on the processes of interaction between switches and controller. However, the service time and hence the processing delays for packet flows are not regulated by the suppliers of switching equipment (e.g., Cisco Catalyst 3750 switches), which makes it difficult to determine the probabilistic and time characteristics of networks during the design stage and also to formalize any suggestions on improving the performance of network elements. The models presented in this paper are based on the classical queueing theory and Laplace transforms. In this case, the relation between the stages of packets processing on network devices has little significance, and therefore the performance indices are obtained in terms of single-phase network parameters. The mean service time and the mean number of packets in a network are calculated as functions of the load of network devices. Also, analytical expressions for determining the mean loss ratio of network packets at each phase of processing by a switch are derived.

Network congestion is a phenomenon strongly impacting the real level of efficiency expected from the modern network environment. It has direct impact on reliability increase and high, stable throughput. It's also one of the main reasons of the end to end delay increase. These characteristics define the quality of communication channels. Optimizing network nodes configuration for only one of the given features can exacerbate the other parameters. This paper focuses on avoiding and alleviating network congestions using multiobjective optimization. This optimization process is used to adjust the controller gain and optimal reference signal. Unlike in other presented approaches, in this case, a dynamical, discrete, non-stationary model of communication channel is applied. It reflects delay conditions in a real environment, which are varying in time. The advantage of such approach rises from the comparison with analogue stationary models, what is also discussed in this research. Two different control strategies have been chosen to be subjected to the optimization process. The first one uses the constant, optimized reference value. The second one is using an adaptive reference value. The proposed strategy of congestion control is adjusting parameters of the presented models to alleviate undesirable results of sudden, network condition changes. It is obtained by maximization of available bandwidth usage combined with the minimization of buffer utilization. This approach supports avoiding harmful congestion effects like retransmission, packet dropping and high network delay, which eventually cause network throughput degradation.

Live media streaming (audio and video) technology has boosted up in the recent years. With this, the real time data flow over the network has increased and many algorithms are developed to prevent the congestion of the network. Most of the them are developed for TCP network and only few of them for UDP network. Moreover, these algorithms use programming languages such as Python, Java, etc. which has a vast variety of libraries. This paper highlights an algorithm implemented in C++, to control the bit rate of real-time video over UDP network. The streaming is accomplished with simultaneous execution of image streaming and audio steaming, hence realizing video streaming as a whole. The packets are sent into the network in a manner to attain the required bit rate, which is achieved by varying the size of the buffer at sending device. Total travel time of a single frame is estimated using timestamps and average travel time is calculated. Total travel time along with size of buffer together decides the bit rate. Additionally, required network delay is introduced with the help of timer. It is a simple and stand-alone media streaming process, with highly-reduced complexity. The main aim of the paper is to achieve programmable delay and variable bit rate enabled video streaming for space applications.

Nowadays physical objects have an ability to connect to the internet, process, analyze the data to the real world through the Internet of Things (IoT). These physical objects have a limited processing space, storage, memory, and bandwidth. Routing becomes a significant factor throughout the data transmission process in IoT devices. The network layer Routing Protocol for Low power and Lossy Networks (RPL) provides communication and the performance of information exchange among the IoT devices. The RPL protocol was mainly designed to perform data transmission effectively in the network with low dynamic traffic. Then, in large network traffic, heavy dynamic traffic scenarios it encounters network congestion problems due to load imbalance distribution. Several RPL load balancing routing algorithms have existed intended to improve network reliability. The Expected Transmission Count and Parent count (ETXPC) objective function is one of the load balancing objective functions that have been evaluated in a burst traffic network scenario. However, this study evaluates the performance of ETXPC objective function in the RPL general IoT network with regards to different network densities. For this study, we applied the parent count (PC), expected transmission count (ETX), and Buffer Occupancy (BO) routing metrics to guide the data packets. The performance metrics considered for evaluation were energy consumption, the ratio of packets delivered, control overhead, and end-to-end delay. In large traffic network ETXPC provides unreliable links that decrease packet delivered successfully and increases control overhead. The algorithm balances the load distribution such a way that it reduces the network delay and energy consumption in the general IoT network.

This paper focuses on the quantized consensus problem of discrete-time second-order multi-agent systems which suffer from bounded process noise. In such systems, neighboring agents exchange information through a digital network that has only a finite bit rate and is subject to bounded network delay. Network delay may exacerbate the effects of quantization error and degrade the control performance by reducing the information to be extracted from receiving time instants of exchange packets. To handle network delay and save communication bit rates, an event-triggered distributed control protocol based on a dynamic encoding–decoding policy is proposed. Under the proposed event-triggered protocol, the asymptotic consensus of the concerned noise-free multi-agent systems can be achieved at a lower bit rate than that of periodic sampling control. This bit rate superiority results from the fact that our protocol makes use of the extra information being extracted from the event-triggering time instants. Moreover, it is shown that no higher bit rate is required to guarantee the desired consensus in the existence of bounded process noise. Simulations are done to demonstrate the effectiveness of our consensus protocol. As shown by our numerical examples, the occupied bit rate to ensure the desired consensus can be saved by up to 50% compared with periodic sampling strategies.

The classical definition of network delay has been recently augmented by the concept of information timeliness, or Age of Information (AoI). We analyze the network delay and the AoI in a multi-hop satellite network that relays status updates from satellite 1, receiving uplink traffic from ground devices, to satellite K, using K-2 intermediate satellite nodes. The last node, K, is the closest satellite with connectivity to a ground station. The satellite formation is modeled as a queue network of M/M/1 systems connected in series. The scenario is then generalized for the case in which all satellites receive uplink traffic from ground, and work at the same time as relays of the packets from the previous nodes. The results show that the minimum average AoI is experienced at a decreasing system utilization when the number of nodes is increased. Furthermore, unloading the first nodes of the chain reduces the queueing time and therefore the average AoI. These findings provide insights for designing multi-hop satellite networks for latency-sensitive applications., Comment: 6 pages, 9 figures

Vehicular Adhoc Networks (VANETs) ensures road safety by communicating with a set of smart vehicles. VANET is a subset of Mobile Adhoc Networks (MANETs). VANET enabled vehicles helps in establishing communication services among one another or with the Road Side Unit (RSU). Information transmitted in VANET is distributed in an open access environment and hence security is one of the most critical issues related to VANET. Although each vehicle is not a source of all communications, most contact depends on the information that other vehicles receive from it. That vehicle must be able to assess, determine and respond locally on the information obtained from other vehicles to protect VANET from malicious act. Of this reason, message verification in VANET is more difficult due to the protection and privacy issues of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.

Network microbursts, which are sub-millisecond order bursts of traffic, have gathered attention due to causing network delay and packet loss. However, there are two problems in analyzing the causes of microbursts: how to capture the packet included in the microburst and how to specify the flows causing microbursts. To resolve these problems, we propose a field-programmable gate array (FPGA)-based microburst analysis system. This system detects microbursts with dedicated hardware in sub-millisecond time resolution. It can capture only packets before and after microburst detection triggered by detection with a static threshold for whole traffic. In addition, it can specify the flows causing microbursts by detection with a dynamic threshold for each flow. The experimental results show that the proposed system can capture only packets before and after microburst detection and can correctly specify the flow causing microbursts even in a network with fluctuating bandwidth usage in practical traffic conditions on the basis of network trace data in a datacenter. The proposed system is implemented with Intel® PAC with Arria® 10 GX FPGA and consumes relatively small amounts of hardware resources: 51 % ALMs, 16 % registers, and 57 % block memories.

The topology of a network is fundamental for building network infrastructure functionalities. In many scenarios, enterprise networks may have no desire to disclose their topology information. In this paper, we aim at preventing attacks that use adversarial, active end-to-end topology inference to obtain the topology information of a target network. To this end, we propose a Proactive Topology Obfuscation (ProTO) system that adopts a detect-then-obfuscate framework: (i) a lightweight probing behavior identification mechanism based on machine learning is designed to detect any probing behavior, and then (ii) a topology obfuscation design is developed to proactively delay all identified probe packets in a way such that the attacker will obtain a structurally accurate yet fake network topology based on the measurements of these delayed probe packets, therefore deceiving the attacker and decreasing its appetency for future inference. We show that ProTO is very effective against active topology inference with minimum performance disruption. Experimental results under different evaluation scenarios show that ProTO is able to (i) achieve a detection rate of 99.9% with a false alarm of 3%, (ii) effectively disrupt adversarial topology inference and lead to the topology inferred by the attacker close to a fake topology, and (iii) result in an overall network delay performance degradation of 1.3% - 2.0%.

In this study, we aim to understand the effect of multiple Version Number Attackers (VNA) in RPL (IPv6 Routing Protocol for Low Power and Lossy Networks) -based Internet of Things (IoT) networks. VNA is one of the most detrimental Denial of Service (DoS) attack that targets the availability of IoT networks. Almost all of the studies targeting the VNA considered a single attacker. However, once an attacker has chance to compromise a node in the network, it may easily compromise more devices, thus 1) affect the performance of the network more and misuse the resources quicker, 2) circumvent the existing security mechanisms and 3) perform other attacks which require more than one malicious node (e.g., wormhole, etc.). Therefore, we have to take multiple attackers into account when designing security systems. In this work, we analyze the effect of multiple attackers from various points of view. Based on extensive simulations and analysis, we conclude that increasing the number of attackers affects only the packet delivery ratio and does not affect average network delay and average power consumption. Our results also show that attacking positions closer to the root cause longer delays and higher power consumption results while central attacking positions are more effective on the packet delivery ratio. Lastly, we evaluate the performance of a recently proposed mitigation technique against multiple attackers.

This article investigates the input-to-state stability (ISS) of continuous-time networked control systems with model uncertainty and bounded noise based on event triggering. The feedback loop is closed over an unreliable digital communication network. Feedback packets suffer from network delay and may be dropped in an independent and identically distributed (i.i.d.) way, which may harm to the concerned stability. This article focuses on a Lyapunov-based event-triggered control design scheme with the consideration of i.i.d. packet dropouts. By designing a state-dependent event-triggering threshold and updating methods, it can still ensure ISS for the concerned multidimensional system in the presence of i.i.d. packet dropouts and model uncertainty without exhibiting the Zeno behavior. Simulations are done to verify the effectiveness of the achieved results.

MANET nodes move continuously in a limited area with a random mobility speed. MANET forms a dynamic topology and the nodes only know neighboring nodes if they are within range of the radio. If the nodes are out of range, it is not possible to communicate with this node in the network. Dynamic topology training is also possible in decentralized MANET. The AOMDV routing protocol in MANET establishes several routes between the sender and the receiver. The performance of the multi-route protocol is better than that of the single route protocol. The multiple routes are better by providing multiple routes between the sender and the receiver, but do not retain the location information of the nodes in the network. In this investigation, we proposed DREAM Multipath Routing (DMR) based on the hybrid location in MANET. The DREAM protocol improves the routing performance of multiple routes by maintaining the location information of the mobile nodes. The DREAM protocol reduces the overhead by forwarding packet floods in MANET, which also reduces network delay. The proposed DMR performance is better than normal AOMDV routing. THE DREAM is to keep the location information with the mobility speed of the nodes. The expected possibility of node movement is calculated and flooding of routing packets is also minimized. Package flooding takes place at the intended location of the destination. The position of the number of nodes changes continuously and the DREAM works on each node. Each node stores the location information of all nodes in the neighborhood of the network. The proposed hybrid DMR protocol improves routing performance and offers better data transmission in a dynamic network.

Wireless sensor networks (WSNs) have attracted quite care because of their wide potential applications. In WSN, energy is a significant key factor of a sensor node since it is an essential function in the lifetime of a network. Retransmissions caused via collision as well as interference during the transaction between sensor nodes growth complete network delay. Because the network delay increases as the node's waiting time increases, the network function is reduced. To solve these problems, link quality and energy-aware (LQEA) metric-based routing strategy in WSN is proposed. In this strategy, the sensor energy and quality of link metrics decide the relay selection in WSN. This strategy evaluates the node-link quality by weighted throughput; the node updated transmission count; available bandwidth; and channel idle ratio to calculate the weighted throughput. This work is simulated in NS-2 simulator and the results confirm that LQEA strategy improved the energy efficiency and packet delivery ratio in WSN.

The basis of smart city services is urban sensing, i.e., the collection of data from sensors deployed in urban settings. However, it has been recently when mobile objects have started to be monitored to feed smart applications within the smart city ecosystems. Most of the advances in this area focus on gathering data from public transportation services such as buses or trams, which use to employ regular cellular or WiFi-based networks and state-of-the-art TCP/IP protocols. Although this covers a basic on-board equipment to report position and other sensor data, there is not a direct integration of Internet of Things (IoT) capabilities in urban transportation and, in general, in moving “things”. Communication technologies used in regular wireless sensor networks such as ZigBee or Bluetooth are not appropriate for high mobility patterns. The Internet of Vehicles (IoV) paradigm aims at filling these gaps, exploiting the synergy between IoT and vehicular networks. It is an emerging area in which extra efforts should be paid to cover constrained capabilities of end devices installed on personal mobility devices, e.g., scooters, segways or bicycles. The solution proposed here deals with the connectivity of moving objects by developing an on-board unit for energy-constrained vehicles, integrating communication capabilities using the Low-Power Wide Area Network (LPWAN) technologies Long Range Wide-area Network (LoRaWAN) and Narrow-Band IoT (NB-IoT) to efficiently collect data. A set of sensors are included to target services in the areas of urban mobility, enhanced environmental perception and speed-up of city operations. The on-board unit has been evaluated through real-life experiments to study especially its communication capabilities with NB-IoT, obtaining good performances in terms of packet delivery ratio and network delay under non-ideal coverage settings. Results show that NB-IoT presents better performances than LoRaWAN, as compared to our previous experiences, with negligible packet loss rates and two-way delays under 100 ms with Internet nodes, at the expense of needing a proper operator network deployment and consuming extra energy due to NB-IoT protocol complexity.

AQM router aims primarily to control the network congestion through marking/dropping packets which are used as congestion feedback in traffic sources to balance their flow rate. However, stabilizing queuing delay and maximizing link utilization have been considered as the main control objectives, especially in media dominated networks. Usually, most of the AQM algorithms are designed for a nominal operating point. However, time-varying nature of network parameters frequently violates their robustness bounds. In this paper, a self-tuning compensated PID controller is proposed to address the time-varying nature of network conditions caused by parameter variations and unresponsive connections. The proposed scheme consists of network parameter estimation and a self-tuning AQM. Traffic load, network delay, and bottleneck link capacity are the time-varying network parameters whose variation effects should be compensated by the controller gains adaptation. As the controller gains are simply and directly obtained from the dynamic model, the obtained self-tuning controller can reasonably adapt itself to different operating conditions, while preserving the simplicity of the PI controllers. Packet-level simulations using ns2 show the outperformance of the developed controller for both latency regulation and resource utilization.

In the recent years, vehicular adhoc networks (VANETs) can be an attractive choice for collecting and transferring the healthcare data of the passengers to the remote healthcare centers. In VANETs, some of the intermediate nodes may act as relay nodes in which case, these networks are called as vehicular relay networks (VRNs). However, the transmitted information in VRNs can be captured by intruders during transmission. Moreover, an attacker can launch selective forwarding, blackhole, and sinkhole attacks in the network, which may in turn degrade the network performance parameters like high end-to-end delay, low packet delivery ratio (PDR) and network throughput. Hence, to address these issues, a secure data dissemination scheme using VRNs is proposed. In the proposed scheme, first, a secure vehicular medical relay network system is designed for the users belonging to disconnected rural areas. The collected information is filtered at zonal levels before transmission to a nearby road side units, which further pass it to the incoming vehicles. Second, a secure passenger health monitoring network is designed which continuously monitors health services of the passengers traveling in different vehicles. The information collected through small body sensors installed in the vehicles act as data sets that is forwarded to the on-board monitoring unit within the vehicle. This collected data is then transmitted to centralized healthcare centers for processing by using VRNs. Lastly, a strong elliptic curve cryptography-based cryptographic solution is designed for secure communication among different vehicles. The performance of the proposed scheme is evaluated in various network scenarios with respect to different selected parameters, such as throughput, network delay, PDR, jitter, transmission and computation overheads, and key distribution overhead. The obtained results indicate that the proposed scheme provides improvement of 52% in average delay and 5% in PDR. This further indicates effective message delivery even with high mobility of the vehicles.

Reduction of end-to-end network delay is an optimization task with applications in multiple domains. Low delays enable improved information flow in social networks, quick spread of ideas in collaboration networks, low travel times for vehicles on road networks, and increased rate of packets in the case of communication networks. Delay reduction can be achieved by both improving the propagation capabilities of individual nodes and adding additional edges in the network. One of the main challenges in such network design problems is that the effects of local changes are not independent, and as a consequence, there is a combinatorial search-space of possible improvements. Thus, minimizing the cumulative propagation delay requires novel scalable and data-driven approaches. We consider the problem of network delay minimization via node upgrades. We show that the problem is NP-hard and prove strong inapproximability results about it (i.e., APX-hard) even for equal vertex delays. On the positive side, probabilistic approximations for a restricted version of the problem can be obtained. We propose a greedy heuristic to solve the general problem setting which has good quality in practice, but does not scale to very large instances. To enable scalability to real-world networks, we develop approximations for Greedy with probabilistic guarantees for every iteration, tailored to different models of delay distribution and network structures. Our methods scale almost linearly with the graph size and consistently outperform competitors in quality. We evaluate our approaches on several real-world graphs from different genres. We achieve up to two orders of magnitude speed-up compared to alternatives from the literature on moderate size networks, and obtain high-quality results in minutes on large datasets while competitors from the literature require more than four hours.

This technical note considers the input-to-state stability of a continuous-time linear system, which sends feedback signals from the sensor to the controller over a nondeterministic communication network with bounded network delay and occasional dropouts. Not only the bits inside each feedback packet, but also the sampling time instant of that packet can carry information. Due to processing delay and network delay, the controller cannot precisely know the sampling time of a packet and can only approximate it with the receive time instant of that packet. The difference between the sampling and receive time instants of a packet is referred to as timing error, which reduces the amount of information extracted from the receive time instant. By additionally extracting information from receive time instants of feedback packets, we may stabilize the concerned system in the input-to-state sense at a lower bit rate than the approaches utilizing only the bits inside feedback packets. Besides timing error, nondeterministic dropouts of feedback packets may also harm the stability of the concerned system. By simultaneously considering timing error and feedback dropouts, we derive some stabilizing bit-rate conditions that are determined by the unstable eigenvalues of the system matrix, the dropout rate, and the upper bound on the timing error. These stabilizing bit-rate conditions are not disturbed by bounded process noise.

The transmission capacity (TC) of the wireless ad-hoc network is one of the key properties to ensure the quality of network service. The main goal of this study is to explore the mobility of the network to establish a node mobile model with a saturated state and determine the TC and delay for the network based on the bidirectional full-duplex (BFD) system. Considering the outage probability, packet arrival rate, and failure probability of packet attempting transmission, the node mobility models are established based on the node mobility and network self-organization. In analysis, this study determines the mobile state, static state, communication state, and outage stat by defining the mobile rule and range of nodes, then the steady-state probability of each state is obtained by calculating the state transition matrix. After optimising the existing saturated Markov model, the authors determine the network delay and TC based on the mobile nodes, contention window size, maximum number of given packet attempting retransmission, back-off time and signal-interference ratio (SIR) etc. The theoretical analysis and numerical simulation show that the TC of the bidirectional ad-hoc network can be significantly improved by using space resources in two directions, and the introduction of the BFD system is indeed to reduce the network delay.

Network errors such as packet losses consume large amounts of energy. We analyzed the reason for this through measurements using the latest smartphones and full-system simulation. We found that on packet losses the smartphones maintain high frequencies for CPU without doing useful work. To address this problem, we propose a method for reducing the energy consumption by lowering the performance level by exploiting a dynamic voltage and frequency scaling mechanism when long network delays are expected. According to our experiments, our method reduces the total energy consumption of web browsing on two different smartphones by up to 10.0% and 11.5%, respectively.

In several domains, the flow of data is governed by an underlying network. Reduction of delays in end-to-end data flow is an important network optimization task. Reduced delays enable shorter travel times for vehicles in road networks, faster information flow in social networks, and increased rate of packets in communication networks. While techniques for network delay minimization have been proposed, they fail to provide any noticeable reduction in individual data flows. Furthermore, they treat all nodes as equally important, which is often not the case in real-world networks. In this paper, we incorporate these practical aspects and propose a network design problem where the goal is to perform k network upgrades such that it maximizes the number of flows in the network with a noticeable reduction in delay. We show that the problem is NP-hard, APX-hard, and non-submodular. We overcome these computational challenges by designing an importance sampling based algorithm with provable quality guarantees. Through extensive experiments on real and synthetic data sets, we establish that importance sampling imparts up to 1000 times speed-up over the greedy approach, and provides up to 70 times the improvement achieved by the state-of-the-art technique.

Measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time (RTT) as the metric. We show that two of the most popular measurement apps—Ookla Speedtest and MobiPerf—have their RTT measurements inflated. We build three test apps for three common measurement methods and evaluate them in a testbed. We overcome the main challenge of obtaining a complete trace of packets and their timestamps using multiple sniffers and frame-based synchronization. Our multi-layer analysis reveals that the delay inflation can be introduced both in the user space and kernel space. The long path of subfunction invocations accounts for the majority of the delay overhead in the Android runtime (both Dalvik VM and ART), and the sleeping functions in the drivers are the major source of the delay overhead between the kernel and physical layer. We propose and implement a native measurement app to mitigate the delay overhead in the Android runtime, and the resulted delay inflation in the user space can be kept under 1.5 ms for almost all cases.