Time Delay Characteristic of Industrial Wireless Networks Based on IEEE 802.15.4a

The IEEE 802.15.4a standard provides a framework for low-data-rate communication systems,typically sensor networks.In this paper,we established a realistic environment for the time delay characteristic of industrial network based on IEEE 802.15.4a.Several sets of practical experiments are conducted to study its various features,including the effects of 1） numeral wireless nodes,2） numeral data packets,3） data transmissions with different upper-layer protocols,4） physical distance between nodes,and 5） adding and reducing the number of the wireless nodes.The results show that IEEE 802.15.4a is suitable for some industrial applications that have more relaxed throughput requirements and time-delay.Some issues that could degrade the network performance are also discussed.

Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks

In industrial wireless networks,data transmitted from source to destination are highly repetitive.This often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet loss.Therefore,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless networks.The term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for mating.As a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base station.This includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue delay.These features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue delay.Statistical analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and efficiency.The results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC 2014.Furthermore,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ANOVA.Moreover,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other algorithms.Simulation results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet loss.The proposed algorithm is also evaluated for the conflicting objectives at Pareto Front,and its analysis reveals that DMOA finds a compromising solution between the objectives,thereby optimizing queue delay.In addition,DMOA on the Pareto front delivers much greater performance when it comes to optimizing the queuing delay for industry wireless networks.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Multi-agent deep reinforcement learning for end—edge orchestrated resource allocation in industrial wireless networks

Edge artificial intelligence will empower the ever simple industrial wireless networks(IWNs)supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices(MTDs)and edge servers.In this paper,we propose a multi-agent deep reinforcement learning based resource allocation(MADRL-RA)algorithm for end-edge orchestrated IWNs to support computation-intensive and delay-sensitive applications.First,we present the system model of IWNs,wherein each MTD is regarded as a self-learning agent.Then,we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of delay and energy consumption.Next,we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision,computation capacity,and transmission power.To break the time correlation of training data while accelerating the learning process of MADRL-RA,we design a weighted experience replay to store and sample experiences categorically.Furthermore,we propose a step-by-stepε-greedy method to balance exploitation and exploration.Finally,we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments,showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.

Novel MAC Layer Proposal for URLLC in IndustrialWireless Sensor Networks

Ultra-reliable and low-latency communications(URLLC) has become a fundamental focus of future industrial wireless sensor net-works(IWSNs). With the evolution of automation and process control in industrial environments, the need for increased reliabilityand reduced latencies in wireless communications is even pronounced. Furthermore, the 5G systems specifically target the URLLCin selected areas and industrial automation might turn into a suitable venue for future IWSNs, running 5G as a high speed inter-process linking technology. In this paper, a hybrid multi-channel scheme for performance and throughput enhancement of IWSNsis proposed. The scheme utilizes the multiple frequency channels to increase the overall throughput of the system along with theincrease in reliability. A special purpose frequency channel is defined, which facilitates the failed communications by retransmis-sions where the retransmission slots are allocated according to the priority level of failed communications of different nodes. Ascheduler is used to formulate priority based scheduling for retransmission in TDMA based communication slots of this channel.Furthermore, in carrier-sense multiple access with collision avoidance(CSMA/CA) based slots, a frequency polling is introducedto limit the collisions. Mathematical modelling for performance metrics is also presented. The performance of the proposed schemeis compared with that of IEEE802.15.4e, where the performance is evaluated on the basis of throughput, reliability and the num-ber of nodes accommodated in a cluster. The proposed scheme offers a notable increase in the reliability and throughput over theexisting IEEE802.15.4e Low Latency Deterministic Networks(LLDN) standard.

Comprehensive Analysis of Secure Data Aggregation Scheme for Industrial Wireless Sensor Network

As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more challenges.Currently,the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating,alternately,the aim at enhancing the security of data.The performances of the secure data aggregation protocols are the trade-off of several metrics,which involves the transmission/fusion,the energy efficiency and the security in Wireless Sensor Network(WSN).Unfortunately,there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in WSN.In consideration of IWSN,we firstly review the security requirements and techniques in WSN data aggregation in this paper.Then,we give a holistic overview of the classical secure data aggregation protocols,which are divided into three categories:hop-by-hop encrypted data aggregation,end-to-end encrypted data aggregation and unencrypted secure data aggregation.Along this way,combining with the characteristics of industrial applications,we analyze the pros and cons of the existing security schemes in each category qualitatively,and realize that the security and the energy efficiency are suitable for IWSN.Finally,we make the conclusion about the techniques and approach in these categories,and highlight the future research directions of privacy preserving data aggregation in IWSN.

Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

The reliability and real time of industrial wireless sensor networks （IWSNs） are the absolute requirements for industrial systems, which are two fore- most obstacles for the large-scale applications of IWSNs. This paper studies the multi-objective node placement problem to guarantee the reliability and real time of IWSNs from the perspective of systems. A novel multi-objective node deployment model is proposed in which the reliabil- ity, real time, costs and scalability of IWSNs are addressed. Considering that the optimal node placement is an NP-hard problem, a new multi-objective binary differential evolu- tion harmony search （MOBDEHS） is developed to tackle it, which is inspired by the mechanism of harmony search and differential evolution. Three large-scale node deploy- ment problems are generated as the benCHmarks to verify the proposed model and algorithm. The experimental results demonstrate that the developed model is valid and can be used to design large-scale IWSNs with guaranteed reliability and real-time performance efficiently. Moreover, the comparison results indicate that the proposed MOB- DEHS is an effective tool for multi-objective node place- ment problems and superior to Pareto-based binary differential evolution algorithms, nondominated sorting genetic algorithm II （NSGA-II） and modified NSGA-II.

In-Depth Study of Potential-Based Routing and New Exploration of Its Scheduling Integration

Industrial wireless mesh networks(WMNs)have been widely deployed in various industrial sectors,providing services such as manufacturing process monitoring,equipment control,and sensor data collection.A notable characteristic of industrial WMNs is their distinct traffic pattern,where the majority of traffic flows originate from mesh nodes and are directed towards mesh gateways.In this context,this paper adopts and revisits a routing algorithm known as ALFA(autonomous load-balancing field-based anycast routing),tailored specifically for anycast(one-to-one-of-many)networking in WMNs,where traffic flows can be served through any one of multiple gateways.In essence,the scheme is a hybrid-type routing strategy that leverages the advantages of both back-pressure routing and geographic routing.Notably,its novelty lies in being developed by drawing inspiration from another field,specifically from the movement of charges in an electrostatic potential field.Expanding on the previous work,this paper explores further in-depth discussions that were not previously described,including a detailed description of the analogy between an electrostatic system and a WMN system based on precise mapping perspectives derived from intensive analysis,as well as discussions on anycast,numerical methods employed in devising the ALFA scheme,its characteristics,and complexity.It is worth noting that this paper addresses these previously unexplored aspects,representing significant contributions compared to previous works.As a completely new exploration,a new scheduling strategy is proposed that is compatible with the routing approach by utilizing the potential-based metric not only in routing but also in scheduling.This assigns higher medium access priority to links with a larger potential difference.Extensive simulation results demonstrate the superior performance of the proposed potential-based joint routing and scheduling scheme across various aspects within industrial WMN scenarios.