Data Collision Research Articles

Zonotopic multi-sensor fusion estimation with mixed delays under try-once-discard protocol: A set-membership framework

In this paper, the fusion estimation problem is investigated for a class of multi-sensor networked systems with unknown-but-bounded noises and mixed time-delays under try-once-discard protocols (TODPs). The measurements of multiple sensor nodes are sent to the remote fusion center through individual network channels, where the TODP is implemented in each channel to schedule the signal transmissions for the purpose of avoiding data collisions. To fuse the information collected from all channels, a sequential estimator is proposed whose estimator parameters are designed such that the estimation error (after each measurement update) is restrained into a zonotope with minimum F-radius at each moment. First, by using the properties of zonotopes, desired zonotopes are obtained that contain the estimation errors, and the F-radii of these zonotopes are subsequently minimized by appropriately designing the estimator parameters. It is shown that, under the proposed zonotopes-based fusion rule, the designed sequential estimator leads to the same estimation accuracy as that of the widely utilized parallel estimator. Finally, an illustrative example is provided to verify the validity of the developed fusion estimation method.

An approximate minimum mean-square error estimator for linear discrete time-varying systems: Handling Try-Once-Discard protocol

This paper is concerned with the remote state estimation problem for a class of linear discrete time-varying stochastic systems under communication constraints. In order to mitigate the undesired phenomena of data collisions, a Try-Once-Discard (TOD) protocol is utilized to regulate the signal transmissions over the sensor-to-estimator communication channel, where the TOD protocol is based on the scheduling rule described by a specific switching function. Under the introduced TOD protocol, the approximate minimum mean-square error (MMSE) state estimation problem is investigated and a recursive algorithm is developed for the MMSE estimator design whose computational complexity is comparable to that of the conventional Kalman filter. Furthermore, some conditions are established for the boundedness of the estimation error covariance. A numerical example is presented to illustrate the effectiveness of the proposed MMSE estimator.

Edge Based Priority-Aware Dynamic Resource Allocation for Internet of Things Networks.

The exponential growth of the edge-based Internet-of-Things (IoT) services and its ecosystems has recently led to a new type of communication network, the Low Power Wide Area Network (LPWAN). This standard enables low-power, long-range, and low-data-rate communications. Long Range Wide Area Network (LoRaWAN) is a recent standard of LPWAN that incorporates LoRa wireless into a networked infrastructure. Consequently, the consumption of smart End Devices (EDs) is a major challenge due to the highly dense network environment characterised by limited battery life, spectrum coverage, and data collisions. Intelligent and efficient service provisioning is an urgent need of a network to streamline the networks and solve these problems. This paper proposes a Dynamic Reinforcement Learning Resource Allocation (DRLRA) approach to allocate efficient resources such as channel, Spreading Factor (SF), and Transmit Power (Tp) to EDs that ultimately improve the performance in terms of consumption and reliability. The proposed model is extensively simulated and evaluated with the currently implemented algorithms such as Adaptive Data Rate (ADR) and Adaptive Priority-aware Resource Allocation (APRA) using standard and advanced evaluation metrics. The proposed work is properly cross validated to show completely unbiased results.

Spectrum Efficient Communication for Heterogeneous IoT Networks

Spectrum sharing for coexistence between heterogenous IoT (Internet of Things) networks degrades data communication effectiveness severely. Cross-Technology Communication(CTC) enables that heterogeneous devices can talk to each other directly, which sheds light on spectrum allocation of heterogeneous IoT networks for efficient communication based on CTC, such as, a WiFi device, as a sophisticated leader, coordinates the spectrum allocation for achieving efficient communication on off-the-shelf devices. In this work, we propose a simple spectrum allocation strategy, which is suitable for any duty-cycles of WiFi and ZigBee devices. Depending on the scheme, heterogeneous devices arrange their working schedules and each device monopolizes its time slots to evade data collisions and reduce wireless interference for achieving efficient communication. We implement a network-layer spectrum allocation technique, called CoWBee, that can be easily deployed in existing WiFi infrastructure without modifying the firmware or hardware of both WiFi and ZigBee devices. CoWBee is evaluated based on our platform and the results show our scheme based on CTC is feasible, which provides valuable insights about the spectrum allocation on achieving desirable performance.

Optimization of LoRa SF Allocation Based on Deep Reinforcement Learning

LoRa is an IoT communication technology that realizes ultra-long-distance transmission through spread spectrum modulation. However, its ultra-long-distance transmission also sacrifices the corresponding rate, and data conflicts are prone to occur when the number of nodes is large. In this article, we investigate various types of data collisions in LoRa wireless work, most of which are affected by Spreading Factor (SF) assignment. At present, the distribution of the SF for LoRa in the industry is mostly based on Min-airtime and Min-distance. In the case of a large number of nodes, the data collision between nodes will increase sharply. This paper proposes a SF redistribution scheme under limited network resources, in order to improve the terminal capacity of the LoRa gateway. First, the problem of minimizing the data collision rate without expanding gateway or network resources is presented. Specifically, the reallocation of SF with increasing number of terminals is studied. Finally, considering the randomness of the data sent by the terminal, SF redistribution schemes based on deep reinforcement learning (DRL) are developed. The simulation results show that the collision rate of the proposed SF redistribution scheme is nearly 30% lower than Min-airtime and Min-distance, and its total energy consumption is close to Min-distance. Therefore, the proposed SF redistribution scheme can effectively improve the gateway capacity of LoRa wireless network.

Minimizing Latency for Data Aggregation in Wireless Sensor Networks: An Algorithm Approach

In wireless sensor networks (WSNs), especially in underwater sensor networks, the problem of reporting data to the sink with minimum latency has been widely discussed in many research works. Many studies address using data aggregation to report the same type of data to the sink without data collision in a short period of time. However, due to the rapid development of sensor technology in recent years, a sensor is allowed to have multiple sensing capabilities, that is, it can generate and collect different types of data. Because different types of data have different meanings and required aggregation functions, only the data that belong to the same type are allowed to be aggregated. In addition, due to the interference of the environment or noise, the links in the WSNs are often not bidirectional. This motivates us to study the problem of using minimum latency scheduling to aggregate and report data to the sink without data collision in multiple-data-type WSNs having unidirectional links, which is shown to be NP-hard in the article. The Relative-Collision-Graph-Based Scheduling Algorithm (RCGBSA) is proposed accordingly. Simulations are conducted to demonstrate the performance of the RCGBSA.

Design of Distance Teaching System for Interior Design Course Based on Internet of Things and Education Platform

Distance education system plays an important role in promoting the integration of professional characteristics and application practice. In order to solve the problem of network throughput decline caused by communication channel data conflict, a distance teaching system of interior design course is designed based on Internet of Things and education platform. In the hardware part, the audio acquisition circuit and video acquisition circuit are designed to ensure the quality of signal transmission. In the software part, the communication architecture is set based on the Internet of Things technology, and a course data anticollision algorithm is proposed, which uses the spread spectrum code to separate the corresponding data to avoid data collision. Based on the education platform, the function structure of distance education system is designed to complete the course management and maintenance. The experimental results show that the maximum throughput of the distance education system designed in this paper is 3.54 MB/s, which is 0.86 MB/s and 0.97 MB/s more than the system based on big data technology and artificial intelligence, and it can effectively avoid the problem of data conflict.

A Real-Time LoRa Protocol Using Logical Frame Partitioning for Periodic and Aperiodic Data Transmission

Owing to provision of a long-range and robust link, LoRa technology has drawn attention for the use in industrial data collection networks. This article proposes a real-time LoRa protocol that can effectively deal with both periodic and aperiodic data. The slots in a frame are logically partitioned such that they are first scheduled for periodic data by a slot scheduling algorithm, and then, the remaining unscheduled slots are used for event-driven aperiodic data. In this logical frame partitioning, the unscheduled slots appear in an interleaved fashion so that aperiodic tasks can transmit data with low delay and fairness while every periodic task still completes data transmission before the beginning of the next period. To deal with the problems of data collision and traffic congestion for aperiodic data, a two-level collision avoidance scheme is proposed that adopts the notion of a contention window and a delay slot. According to simulation, not only can the proposed protocol guarantee the timely delivery of periodic data but it can also deal with aperiodic data with high reliability, fairness, and low delay, compared with other recent protocols.

Design and Implementation of an IoT Gateway for Zigbee and WiFi

This paper proposed a framework called IAPA (Integrated Access Point Architecture) that applied an off-the-shelf AP (Access Point) device on WLAN (Wireless Local Area Network) as an IoT (Internet-of-Things) gateway to integrate Zigbee and Wifi. This Wifi-Zigbee AP (called WiZAP) has the advantages of low cost, small size and low power consumption. However, the coexistence of Wifi and Zigbee in unlicensed bands will introduce a serious data collision problem. In this paper, we propose a linear-time balance-first configuration strategy to adjust Wifi parameters to reduce data collision. A strategy to dynamically configure these parameters for MAC layer communication is also proposed. Experiments test the influence of network transmission in real application scenarios. Results show that our WiZAP can work efficiently with the coexistence of Wifi and Zigbee.

Сoronavirus Pandemic and Expert Knowledge Crisis: Reload of Miracle, Mystery and Authority

The article analyzes the reasons for the important effect of the COVID-19 pandemic, which has become the catalyst for long-overdue decline in the authority of expert knowledge. The author claims that widespread access to information and scientific data results in the collapse of universal and monopolistic expert-scientific hierarchies of knowledge of a large society, controlled by the state. Scientific experts, who acted as the historical heirs of priests and shamans, have lost their privileged access to sacred knowledge, made public by the media and the Internet. This resulted in severe damage to the key function of expertise – legitimization of the political order and power elites. Experts without the status of agents of the state have become indistinguishable from ordinary citizens. The example of discussions between Waxers and Anti-Waxers shows that both sides are able to put forward convincing scientific arguments that rhetorically do not allow the authorities to bring the discussion about the effectiveness of vaccinations down to a completely unobvious dispute between enlightened state experts and uneducated obscurantists. It is in the most developed Western states where one can see a strong civil dissident movement that distrusts or calls into question the disciplinary regimes of collective coexistence, legitimized by the paternalistic rhetoric of concern from political elites. Accordingly, the elites in the background situation of strengthening the practices of heterarchy, post-truth and postmodernism can no longer rely on the usual metanarratives of the Enlightenment, which allowed them to monopolize the discourse of science in the name of progress and unconditional good, building hierarchies of knowledge-power convenient for their priorities. Since science, knowledge and information have long became public domain, the line between elites, experts and citizens in the field of access to science has become almost indistinguishable. The actual political problem is that the situation of collision of different paradigms, opinions and data is exactly the normal state of science, which is now transferred to the field of public discussions following the final secularization of science. Thus, the institution of expert knowledge turns into an unnecessary link in a situation of equal access of all interested parties to scientific data; to an institution that hardly would efficiently perform the functions of scientific legitimation of socially significant decisions in the foreseeable future.

A Low-Profile Programmable Beam Scanning Holographic Array Antenna Without Phase Shifters

Beam scanning antenna brings new opportunities to reduce the data collision of multinode communication in the Internet of Things (IoT). This article proposes a new type antenna design scheme that can be used for IoT relay communication. A programmable beam scanning antenna without phase shifters operating in the X-band is designed to evaluate the feasibility of this scheme. The integrated design of excitation source and phase-modulated structure greatly reduces the profile of the antenna and improves the integrated degrees of freedom with other equipment. By switching the state of the PIN diode loaded on each element, different radiators can be selected and the direction of current polarization on elements can be adjusted to change the radiation phase. Furthermore, the diode states are encoded by the holographic antenna theory and controlled by the FPGA controller to realize programmable beam scanning. One-element, two-element, and 1-D array containing 32 elements are fabricated and measured. The experimental results are in good agreement with the simulation data. The operating bandwidth is 7.5% at 10 GHz while the beam scanning range of the array is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 60{^\circ }$ </tex-math></inline-formula> –60°, with a good scanning accuracy and stability. The proposed low-profile beam scanning antenna can provide stable communication services to multiple users and smart devices as a new type of beam scanning array antenna solution for the IoT application, which is beneficial to this field.

Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems With the Denial-of-Service Attacks and Stochastic Communication Protocol

In this paper, a new filtering fusion problem is studied for nonlinear cyber-physical systems under error-variance constraints and denial-of-service attacks. To prevent data collision and reduce communication cost, the stochastic communication protocol is adopted in the sensor-to-filter channels to regulate the transmission order of sensors. Each sensor is allowed to enter the network according to the transmission priority decided by a set of independent and identically-distributed random variables. From the defenders&#x0027; view, the occurrence of the denial-of-service attack is governed by the randomly Bernoulli-distributed sequence. At the local filtering stage, a set of variance-constrained local filters are designed where the upper bounds (on the filtering error covariances) are first acquired and later minimized by appropriately designing filter parameters. At the fusion stage, all local estimates and error covariances are combined to develop a variance-constrained fusion estimator under the federated fusion rule. Furthermore, the performance of the fusion estimator is examined by studying the boundedness of the fused error covariance. A simulation example is finally presented to demonstrate the effectiveness of the proposed fusion estimator.

Distributed recursive filtering under random access protocols: A multirate strategy

AbstractThis article addresses the distributed recursive filtering problem for a class of time‐varying multirate systems in sensor networks with randomly occurring nonlinearities and the communication protocol. A multirate model is considered where the sampling period of the sensors is slower than the updating period of the system state. Accordingly, the lifting technique is proposed to transform the multirate system into a single‐rate system. In order to avoid data collisions, the random access protocol (RAP) scheduling is applied to determine which signal/packet selected by RAP scheduling can access the shared network for each sensor. Attention is focused on the design of a distributed recursive filter such that, in the simultaneous presence of randomly occurring nonlinearities, the multirate strategy and the RAP scheduling, an upper bound for the filtering error covariance is obtained with the help of the mathematical induction method. Furthermore, by utilizing a novel matrix simplification technique, the filter parameters are recursively calculated by minimizing the obtained upper bound. Finally, the effectiveness of the proposed distributed recursive filtering scheme is demonstrated via a numerical example.

An enhanced adaptive duty cycle scheme for energy efficiency and QoS awareness in wireless sensor networks

A Wireless Sensor Network (WSN) is a small device that has batteries and radios to connect to the internet. The main problem with WSNs is a limited energy source, energy studies needed to ensure that these sensor nodes can last longer, especially since a lot of energy is wasted during idle listening, overhearing, and data collision that occurs at the medium access control (MAC) layer. The common mechanism used for saving energy in WSN, specifically at the MAC layer is the duty cycle schedule. Duty cycling coordinates sleep-wake time sensor nodes to maximize network lifetime while achieving specific application goals such as high throughput or low latency. Duty cycling of every node should be adjusted separately at any runtime depending on the network conditions to achieve desired delay guarantees and energy efficiency. Recently, a few adaptive duty cycle schemes were introduced, these schemes have reduced energy consumption by some degree, this leaves an open end to the degradation of the quality of service. In this study, adaptive duty cycles enhanced with a priority queue where packet size is the parameter to adjust the duty cycle in order to get efficient energy consumption. A variant of packet size tested to ensure optimum quality of services (QoS). These factors determine the duration of a node’s listen period for various packet transmission scenarios and requirements. As the result, the proposed Enhanced S-MAC (ESMAC) shows an improvement in the energy consumption and QoS compared to the default MAC protocol and S-MAC protocol. The success of this project will contribute to the performance improvement of sensing devices.

Research on Spatial-temporal Spread and Risk Profile of the COVID-19 Epidemic Based on Mobile Phone Trajectory Data.

The COVID-19 epidemic poses a significant challenge to the operation of society and the resumption of work and production. How to quickly track the resident location and activity trajectory of the population, and identify the spread risk of the COVID-19 in geospatial space has important theoretical and practical significance for controlling the spread of the virus on a large scale. In this study, we take the geographical community as the research object, and use the mobile phone trajectory data to construct the spatiotemporal profile of the potential high-risk population. First, by using the spatiotemporal data collision method, identify, and recover the trajectories of the people who were in the same area with the confirmed patients during the same time. Then, based on the range of activities of both cohorts (the confirmed cases and the potentially infected groups), we analyze the risk level of the relevant places and evaluate the scale of potential spread. Finally, we calculate the probability of infection for different communities and construct the spatiotemporal profile for the transmission to help guide the distribution of preventive materials and human resources. The proposed method is verified using survey data of 10 confirmed cases and statistical data of 96 high-risk neighborhoods in Chengdu, China, between 15 January 2020 and 15 February 2020. The analysis finds that the method accurately simulates the spatiotemporal spread of the epidemic in Chengdu and measures the risk level in specific areas, which provides an objective basis for the government and relevant parties to plan and manage the prevention and control of the epidemic.

Design and analysis of IEEE 802.11 based Full Duplex WLAN MAC protocol

Full-Duplex (FD) communication is a promising technique that can be used in 5G to potentially double the system throughput compared to conventional Half Duplex (HD) transmission. Recent experiments have demonstrated near-perfect self-interference suppression techniques in the PHY layer to aid the possibility of FD communication. This paper presents a Synchronized Mode Full-Duplex (SM-FD) wireless LAN MAC protocol with appropriate modification of the IEEE 802.11 standard. The proposed SM-FD MAC protocol maintains synchronization between nodes and Access Point (AP) to increase efficiency. SM-FD MAC protocol supports WLAN having both FD and HD capable nodes by establishing both symmetrical links (Node1 ↔ AP) and asymmetrical links (Node1 → AP → Node2). To this end, we have utilized the packet-alignment-based capture effect in an efficient way. The proposed SM-FD MAC protocol has been analyzed using a Markov chain model. The analytical results for RTS and data collision probability, saturated throughput, delay, and energy spent per packet transmission have been derived and validated against simulation.

Resilient energy-to-peak filtering for linear parameter-varying systems under random access protocol

In this paper, we consider the energy-to-peak filtering issue for a class of linear parameter-varying (LPV) systems with time delays subject to certain communication regulation under which only one sensor is allowed to transmit its measurement data at each transmission instant. The data communication is regulated by the random access protocol (RAP) for the purpose of avoiding data collisions. The main purpose of this paper is to design an LPV filter such that the resultant filtering error system is asymptotically stable and also satisfies the prescribed - performance in the mean square. Taking into account both the LPV nature and the possible gain perturbations, a parameter-dependent resilient filter is constructed according to the plant dynamics and scheduling behaviour of the RAP. The desired filter gain matrices are obtained by solving a set of linear matrix inequalities. Finally, a simulation example is given to validate the effectiveness and correctness of the filter design scheme.

Collision-Free Dynamic Convergecast in Low-Duty-Cycle Wireless Sensor Networks

Convergecast is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. This problem further causes not only wasted packet retransmissions, but also a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose an <u>i</u>ncast-<u>co</u>llision-f<u>re</u>e convergecast protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique, establishes a non-conflicting schedule for efficient convergecast, and improves the channel utilization by allowing senders to opportunistically transmit packets once detecting unused slots. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the baseline protocol, iCore effectively minimizes the end-to-end delay by 25&#x0025; &#x007E; 57&#x0025; and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.

Hybrid power control and contention window adaptation for channel congestion problem in internet of vehicles network

Technology such as vehicular ad hoc networks can be used to enhance the convenience and safety of passenger and drivers. The vehicular ad hoc networks safety applications suffer from performance degradation due to channel congestion in high-density situations. In order to improve vehicular ad hoc networks reliability, performance, and safety, wireless channel congestion should be examined. Features of vehicular networks such as high transmission frequency, fast topology change, high mobility, high disconnection make the congestion control is a challenging task. In this paper, a new congestion control approach is proposed based on the concept of hybrid power control and contention window to ensure a reliable and safe communications architecture within the internet of vehicles network. The proposed approach performance is investigated using an urban scenario. Simulation results show that the network performance has been enhanced by using the hybrid developed strategy in terms of received messages, delay time, messages loss, data collision and congestion ratio.

Distributed cubature Kalman filtering for nonlinear systems with stochastic communication protocol

AbstractThis paper is concerned with the distributed cubature Kalman filtering (DCKF) for a class of discrete time‐varying nonlinear systems subject to stochastic communication protocol (SCP). In order to avoid the data collisions, the SCP is introduced to randomly schedule each sensor node information from one of the neighboring nodes to the filter which is presented via not only the information of itself sensor but also the information of neighboring sensors. The considered scheduling probability of the selected node is unknown. Therefore, the exact filtering error covariance is not available. The purpose of this paper is to design a DCKF under the spherical–radial cubature rule such that an upper bound of the filtering error covariance is guaranteed by utilizing fundamental inequality. Such an upper bound is dependent on known upper and lower bounds of the scheduling probabilities. Subsequently, the desired filter matrices are given by minimizing the upper bound of the filtering error. A sufficient condition is derived by using Riccati‐like difference equations method. Besides, a recursive form of filter algorithm is designed for online computation. Finally, the usefulness of the DCKF is verified by utilizing a simulation example on the induction machines.