Unreliable Wireless Network Research Articles

Joint Mobility Control and MEC Offloading for Hybrid Satellite-Terrestrial-Network-Enabled Robots

Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

Joint Scheduling and Robust Aggregation for Federated Localization Over Unreliable Wireless D2D Networks

Joint Scheduling and Robust Aggregation for Federated Localization Over Unreliable Wireless D2D Networks

Novel MAC protocol for handling correlation and dynamics of medical traffic in WBANs

In Wireless Body Area Networks (WBANs), it is crucial to ensure reliable and timely delivery of gathered data, despite their intrinsic challenges related to limited resources at the sensor level. In fact, in the medical context, most real-time and life-critical applications are both delay-sensitive and loss-sensitive; the loss or corruption packets carrying critical data due to unreliable wireless networks could have severe consequences. In this regard, the concept of context awareness can be exploited; the more an application acquires additional information about users, sensors, and their context, the better it can adapt its functional behavior to improve its performance of delay, throughput and packet loss.Thus, acquiring some knowledge about the patient’s medical state over time may enhance the interpretation and intervention of the medical staff. In this context, we propose a dynamic context-aware MAC protocol (DMTM-MAC) that takes into account the dynamic and correlation aspects of medical traffic in order to adjust the channel access mechanisms and the general network behavior. We assign to each sensor a context that reflects the type of traffic generated and its QoS constraints. We aim to track specific biosensors, which are related to vital indicators relevant to a specific disease; the associated measurements may reflect the variation of the patient’s state over time. To such nodes, we assign a new node context, called dynamic context, which can help to predict the eventual health decline over time. The body node coordinator will have the ability to analyze the received data in order to detect abnormalities and disease evolution. Observed changes in collected measurements can be an early predictor of an advanced or complicated situation and can allow this BAN coordinator to handle dynamic context changes in these types of sensors by increasing their priority and transmission frequency to cope with the current situation. Performance evaluation showed that DMTM-MAC outperforms both MTM-MAC and IEEE 802.15.6 in terms of delay and packet delivery ratio. The proposed approach enhances the delay up to 80 % over IEEE 802.15.6.

Event-Triggered Transmission Policies for Nonlinear Control Systems Over Erasure Channels

We investigate the scenario where a controller communicates with a nonlinear plant via a wireless erasure channel. We present an event-based control strategy to stabilize the plant while sporadically using the unreliable wireless network. In particular, control packets may be lost at any time with a certain probability. Consequently, stability is ensured in a stochastic sense. We then compare the proposed event-based policy with a baseline policy that transmits according to the age of information, i.e., the time elapsed since the last successful reception. For any given baseline policy, we show how to design an event-based policy that ensures the same guaranteed control performance while leading, on average, to a strictly smaller channel utilization. Numerical simulations suggest that the achieved channel utilization may in fact be significantly smaller.

Optimization Over Time-Varying Networks and Unbounded Information Delays

Solving optimization problems in multiagent systems involves information exchange between agents. The obtained solutions should be robust to information delays and errors that arise from an unreliable wireless network, which typically connects the multiagent system. In today’s large-scale dynamic Internet of Things style multiagent scenarios, the network topology changes and evolves over time. In this article, we present a simple distributed gradient-based optimization framework and an associated algorithm. Convergence to a minimum of a given objective is shown under mild conditions on the network topology and objective. A key feature of our approach is that we merely assume that the messages sent reach the intended receiver, possibly delayed, with some positive probability. To the best of authors’ knowledge, ours is the first analysis under such weak general network conditions. We also discuss in detail the verifiability of the assumptions involved. This article also makes a technical contribution in terms of the allowed class of objective functions. Specifically, we present an analysis wherein the objective function is such that its sample-gradient is merely locally Lipschitz continuous. The theory developed herein is supported by empirical results.

Radix-Partition-Based Over-the-Air Aggregation and Low-Complexity State Estimation for IoT Systems Over Wireless Fading Channels

We consider remote state estimation for an internet-of-things (IoT) system, where a number of distributed IoT sensors monitor a dynamic plant and deliver the state measurements to a remote state estimator over an unreliable wireless network. We propose novel radix-partition-based over-the-air aggregation to coordinate the transmissions of the IoT sensors, which strongly enhances the spectral efficiency of radio resources and enables a smaller state estimation mean square error (MSE) at the remote state estimator, taking into consideration the quantization effect and radio resource allocation for the IoT sensors. We consider a fixed-filtering design at the remote state estimator, which significantly reduces the signal processing overhead at the remote state estimator compared to using the conventional Kalman-filtering-based state estimation approach. We show that the proposed low-complexity state estimation scheme enables state estimation stability via the linear matrix inequality (LMI) technique for on-off fading channels and via Lyapunov stability analysis for random fading channels. Based on this, we further propose efficient algorithms for the offline design of the filtering gain, connection topology and radix representation for IoT systems. Numerical results show that the proposed scheme has superior scalability performance and can achieve a smaller state estimation MSE compared to various state-of-the-art baselines.

A Mobility Management Algorithm in the Internet of Things (IoT) for Smart Objects based on Software-Defined Networking (SDN)

In recent decades, technological advancements have significantly improved people's living standards and given rise to the rapid development of intelligent technologies. The Internet of Things (IoT) is one of the most important research topics worldwide. However, IoT is often comprised of unreliable wireless networks, with hundreds of mobile sensors interconnected. A traditional sensor network typically consists of fixed sensor nodes periodically transmitting data to a pre-determined router. Current applications, however, require sensing devices to be mobile between networks. We need mobility management protocols to manage these mobile nodes to provide uninterrupted service to users. The interactions between the mobile nodes are affected by the loss of signaling messages, increased latency, signaling costs, and energy consumption because of the characteristics of these networks, including constrained memory, processing power, and limited energy source. Hence, developing an algorithm for managing smart devices' mobility on the Internet is necessary. This study proposes an efficient and effective distributed mechanism to manage mobility in IoT devices. Using Software-Defined Networking (SDN) based on the CoAP protocol, the proposed method is intended not only to reduce the signaling cost of messages but also to make mobility management more reliable and simpler.

Message Passing Over Cloud Using Cascade Ciphering With Randomized Algorithm

To solve the problem of poor security and performance caused by traditional encryption algorithm in the cloud data storage, we propose a cascade ciphering with randomized algorithm. The multiple encryption is a process to encrypt the data multiple times using the same algorithm or different algorithms, to provide multilayer and multilevel security over unreliable wireless network during communication. The encryption algorithms are AES, MD5, SHA 129, SHA 256, Whirlpool and Blowfish.

MPC Based Power Allocation for Reliable Wireless Networked Control Systems

This paper considers a wireless networked control system (WNCS) where the controller and controlled object (plant) are connected via an unreliable wireless network. The control system should be designed considering the reliability of the given network while its network reliability is controllable by changing transmission power. Thus, this paper provides a joint optimization of transmission power and control-input for the focused WNCS, which is a kind of cross-layer design of communication and control layers. This work assumes that the total transmission power for the feedforward (from the controller to plant) and feedback (from the plant to controller) channels is limited and can be adaptively allocated to each wireless channel. If more transmission power is allocated to the feedforward channel, the control-input can be more reliably transmitted, while the control-state is less reliably transmitted over the feedback channel. Furthermore, the appropriate power allocation depends on the control-state and the previous communication results. Considering the above facts, this paper proposes a model-predictive control (MPC) based joint optimization and shows that the proposed system can provide appropriate power allocation and control-input and thus can enhance the quality of control.

Nelder Mead-Based Spider Monkey Optimization for Optimal Power and Channel Allocation in MANET

The Mobile Ad-hoc Network (MANET) includes both the optimal allocation of shared channels and power in a network. Hence, obtaining the trade-off between energy consumption and delay is the major challenge. The source-constrained and maximizing lifetime of the unreliable wireless network includes optimal power allocation. We proposed an Integrated Spider Monkey Optimization Algorithm (ISMOA) for the minimization of energy consumption and the enhancement of throughput in MANET. The Nelder Mead Model (NMM) is used to improve the performance of the local leader stage in the spider monkey optimization algorithm. In this work, the novel approach is used to improve the performance of total energy consumption, throughput, and delay. The experimental works are executed in NS-2 software. The experimental results demonstrate the low delay, energy consumption with high throughput performance. Moreover, the proposed method outperforms the optimal performance compared to state-of-the-art methods.

Honesty based democratic scheme to improve community cooperation for Internet of Things based vehicular delay tolerant networks

AbstractMany Internet of Things (IoT) applications have been developed and implemented on unreliable wireless networks like the delay tolerant network (DTN); however, efficient data transfer in DTN is still an important issue for the IoT applications. One of the application areas of DTN is vehicular delay tolerant network (VDTN) where the network faces communication disruption due to lack of end‐to‐end relay route. It is challenging as some of the nodes show selfish behavior to preserve their resources like memory and energy and become noncooperative. In this article, an honesty‐based democratic scheme (HBDS) is introduced where vehicles with higher honesty level are elected as heads—during the process. Vehicles involved in the process would maximize their rewards (reputation) through active participation in the network activities, whereas vehicles with noncooperative selfish behavior are punished. The honesty level of the heads is analyzed using Vickrey, Clarke, and Groves (VCG) model. The mathematical model and algorithms developed in the proposed HBDS technique are simulated using the VDTNSim framework to evaluate their efficiency. The performance results show that the proposed scheme dominates current schemes in terms of packet delivery probability, packet delivery delay, number of packets drop, and overhead ratio.

Stability and Convergence of a Message-Loss-Tolerant Rendezvous Algorithm for Wireless Networked Robot Systems

This article considers the problem of swarm rendezvous when robots exchange local coordination messages through an unreliable wireless communication network (i.e., message-loss occurs). First, a discrete-time second-order consensus algorithm is provided to deal with the effects of unreliable channels on wireless networked robot (WNR) systems, and drive the robots to a given geometric formation. Then, necessary and sufficient conditions for the controlled WNR to converge to a desired formation, even in the presence of messages loss, are formulated and simulatively validated. The proposed conditions provide to the WNR designer a minimal topology connectivity requirement, and a relation among algorithm step-size and control gains to be fulfilled for guaranteeing swarm rendezvous.

Effective Bandwidth Prediction through Statistical Technique over Heterogeneous Networks

Real time Streaming Media (Video Streaming) applications are mostly popular on the mobiles and computers using Internet. Due to higher demand of video streaming through wireless network and mobile devices, video are being transmitted through various heterogeneous networks so as to efficiently deliver to the clients devices. This has resulted in the lower quality of real time video, since real time streaming media has quality of parameter requirements like high bandwidth, low packet loss ratio, higher delay and jitter. Streaming media such as video through heterogeneous networks has more challenges due to unreliable wireless networks and device mobility; moreover bandwidth, delay and loss are unknown in advance and are unbounded. In this paper, effective bandwidth prediction through statistical technique over heterogeneous wireless communication networks is proposed. Statistical technique offers computationally efficient bandwidth prediction with reasonably better accuracy. Especially with mobile devices with limited computational power and battery life, necessitates better bandwidth prediction with efficient but computationally simpler algorithms. Bandwidth predictions assist in selecting effective network for video streaming when various heterogeneous networks are available. Detailed bandwidth prediction algorithm is presented with use of quality of service (QoS) parameters data sets available online.

Energy-Efficient and Robust Tensor-Encoder for Wireless Camera Networks in Internet of Things

With the development of Internet of Things (IoT), wireless camera networks have been widely deployed owing to its low-cost of deployment and maintenance as well as flexibility. However, it is challenging for wireless camera networks to provide energy-efficient and robust video transmissions, since the multihop wireless communications have limited bandwidth and low link quality. Besides, the camera nodes are usually battery-powered. State-of-the-art coding schemes adopt complex predictive encoding methods, thus leading to high complexity and power consumption. In this paper, we propose a novel tensor-encoder (i.e., being a randomly generated mask) for energy-efficient and robust video transmissions over unreliable wireless networks. First, we adopt a novel algebraic framework, i.e., the low-tubal-rank tensor model, to capture the strong spatiotemporal correlations within video data. Secondly, we design a mask-encoder, modeled as a mask-sampling process, that dramatically reduces the transmission burden. Then, we propose an alternating minimization algorithm as the corresponding mask-decoder. Thirdly, we prove that the proposed decoder guarantees exponential convergence to the global optima. For an $n\times n\times t$ video stream with tubal-rank $r\ll n$ , the required sampling complexity is $O(nr^2t\;\log^3\;n)\ll n^2t$ and the computational complexity is $O(n^2rt\;\log^2\; n)$ . Finally, based on synthetic data, real-world data and our wireless camera network testbed, we compare the proposed scheme with existing methods and obtain high quality video transmissions at a compression ratio of 20 percent.

String Stability Analysis for Vehicle Platooning Under Unreliable Communication Links With Event-Triggered Strategy

Vehicle platooning systems are often equipped with vehicle-to-vehicle (V2V) communication technologies to improve both the road efficiency and road safety by exchanging vehicle information over wireless networks to maintain relatively small inter-vehicle distance. The road safety often relies on the quality of service delivered by the V2V communication. The V2V networks are, however, subject to channel fading and often have limited communication bandwidth. The limited bandwidth inevitably compromises the road safety as the vehicular network will easily get congested if the number of vehicles joining the network increases. To address the challenges of ensuring vehicle safety under unreliable wireless network with limited bandwidth, this paper first uses a two-state Markov chain to model the channel fading in vehicular networks. Under the Markov chain model, this paper further develops a novel distributed event triggered strategies under which the vehicle platooning system achieves stochastic $\mathcal {L}_2$ string stability while ensuring an efficient use of the limited communication bandwidth. The simulation results are provided to verify the effectiveness and advantages of the proposed methods.

BNNC: Improving Performance of Multipath Transmission in Heterogeneous Vehicular Networks

Nowadays, multipath transmission scheme in heterogeneous vehicular networks has become an emerging topic. It is a great challenge to overcome the unreliability of heterogeneous wireless network in vehicle-to-ground multipath communication. Many multipath transmission schemes were proposed. However, most schemes do not consider the unreliability of wireless networks and are difficult to deploy in vehicle-to-ground communications. Even though part of the multipath transmission schemes consider the unreliability of wireless networks, their overhead is too large to be deployed in vehicle-to-ground multipath communication. Therefore, we propose a BigNum Network Coding (BNNC) scheme for vehicle-to-ground multipath communication. Compared with the Opportunistic Routing (OR) scheme, BNNC's network resource overhead is smaller. Compared with other network coding schemes, it is a better trade-off between coding flexibility and codec efficiency. In this paper, we propose a brand-new mathematical model for network coding which can effectively improve the reliability of the vehicular networks. Secondly, based on the mathematical model, we design BNNC multipath transmission scheme. Compared with the current network coding scheme, the BNNC scheme considers coding flexibility and codec efficiency while ensuring multipath transmission reliability. Thirdly, we compare BNNC scheme with many current multipath transmission schemes through lots of simulations and real tests. The results show that the BNNC scheme is significantly superior to the other network coding schemes in terms of computational performance. And in terms of the network performance, the BNNC scheme can overcome the unreliability of wireless networks in multipath transmission and it has lower overhead than OR scheme.

Multi-Sensor Estimation for Unreliable Wireless Networks with Contention-Based Protocols

The state estimation plays an irreplaceable role in many real applications since it lays the foundation for decision-making and control. This paper studies the multi-sensor estimation problem for a contention-based unreliable wireless network. At each time step, no more than one sensor can communicate with the base station due to the potential contention and collision. In addition, data packets may be lost during transmission since wireless channels are unreliable. A novel packet arrival model is proposed which simultaneously takes into account the above two issues. Two scenarios of wireless sensor networks (WSNs) are considered: the sensors transmit the raw measurements directly and the sensors send the local estimation instead. Based on the obtained packet arrival model, necessary and sufficient stability conditions of the estimation at the base station side are provided for both network scenarios. In particular, all offered stability conditions are expressed by simple inequalities in terms of the packet arrival rates and the spectral radius of the system matrix. Their relationships with existing related results are also discussed. Finally, the proposed results are demonstrated by simulation examples and an environment monitoring prototype system.

Self-healing group key distribution protocol in wireless sensor networks for secure IoT communications

The Internet of Things(IoT) aims to collect a large amount of data and exchange them over wireless networks by connecting billions of perception objects. Security becomes essential for IoT since unreliable wireless networks often create insecure connections. To ensure the group communication security and improve the communication efficiency, self-healing group key distribution (SGKD) protocols are introduced for unreliable wireless networks. In such scheme, lost session keys can be recovered from the current broadcast messages by the group members, instead of requiring the missing key updating messages to be retransmitted by the group manager. A lot of access-polynomial-based SGKD (AP-SGKD) protocols have been proposed. Unfortunately, almost all of them have secure problems. This paper proposes a new AP-SGKD protocol using double chains and access polynomial. The new protocol is the first AP-SGKD protocol which satisfies all basic security properties with optimal storage requirement. In addition, we propose a method to trade off the session key recovering time for less communication cost. The simulation results show that the new scheme can be applied to Zigbee network since it has a good performance on security, storage efficiency and communication efficiency.

Frontier

In an edge deployment model, Internet-of-Things (IoT) applications, e.g. for building automation or video surveillance, must process data locally on IoT devices without relying on permanent connectivity to a cloud backend. The ability to harness the combined resources of multiple IoT devices for computation is influenced by the quality of wireless network connectivity. An open challenge is how practical edge-based IoT applications can be realised that are robust to changes in network bandwidth between IoT devices, due to interference and intermittent connectivity. We present Frontier , a distributed and resilient edge processing platform for IoT devices. The key idea is to express data-intensive IoT applications as continuous data-parallel streaming queries and to improve query throughput in an unreliable wireless network by exploiting network path diversity : a query includes operator replicas at different IoT nodes, which increases possible network paths for data. Frontier dynamically routes stream data to operator replicas based on network path conditions. Nodes probe path throughput and use backpressure stream routing to decide on transmission rates, while exploiting multiple operator replicas for data-parallelism. If a node loses network connectivity, a transient disconnection recovery mechanism reprocesses the lost data. Our experimental evaluation of Frontier shows that network path diversity improves throughput by 1.3×−2.8×for different IoT applications, while being resilient to intermittent network connectivity.

Renormalization group theory for percolation in time-varying networks

Motivated by multi-hop communication in unreliable wireless networks, we present a percolation theory for time-varying networks. We develop a renormalization group theory for a prototypical network on a regular grid, where individual links switch stochastically between active and inactive states. The question whether a given source node can communicate with a destination node along paths of active links is equivalent to a percolation problem. Our theory maps the temporal existence of multi-hop paths on an effective two-state Markov process. We show analytically how this Markov process converges towards a memoryless Bernoulli process as the hop distance between source and destination node increases. Our work extends classical percolation theory to the dynamic case and elucidates temporal correlations of message losses. Quantification of temporal correlations has implications for the design of wireless communication and control protocols, e.g. in cyber-physical systems such as self-organized swarms of drones or smart traffic networks.