Green Computing for Industrial Wireless Sensor Networks: Energy oriented Cross Layer Modelling

Design, analysis and implementation of a time-bounded spectrum handoff algorithm for real-time industrial wireless sensor and actuator networks

Industrial wireless sensor networks (IWSNs) are a key technology for smart manufacturing. To identify the performance bottlenecks in an IWSN before its real-world deployment, the IWSN must first be evaluated through simulations using an error model which accurately characterizes the wireless links in the industrial scenario within which it will be deployed. However, the traditional error models used in most IWSN simulators are not derived from the real traces observed in industrial environments. Accordingly, this study first measured the transmission quality of IEEE 802.15.4 in a one-day experiment in a manufacturing factory and then used the measurement records to construct a second-order Markov frame-level error model for simulating the performance of an IWSN. The proposed model was incorporated into the simulator of OpenWSN, which is an industrial WSN implementing the related IEEE and IETF standards. The simulation results showed that the proposed error model improved the accuracy of the estimated transmission reliability by up to 12% compared to the original error model. Moreover, the estimation accuracy improved with increasing burst losses.

Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N‐P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

Wireless industrial sensor networks are wireless sensor networks which have been adapted to industrial applications. Most techniques for wireless sensor networks can be applied to wireless industrial sensor networks. However, for industrial applications of wireless industrial sensor networks, new requirements such as real-time, reliable delivery need to be considered. In this paper, we propose EARQ, which is a novel routing protocol for wireless industrial sensor networks. It provides real-time, reliable delivery of a packet, while considering energy awareness. In EARQ, a node estimates the energy cost, delay and reliability of a path to the sink node, based only on information from neighboring nodes. Then, it calculates the probability of selecting a path, using the estimates. When packet forwarding is required, it randomly selects the next node. A path with lower energy cost is likely to be selected, because the probability is inversely proportional to the energy cost to the sink node. To achieve real-time delivery, only paths that may deliver a packet in time are selected. To achieve reliability, it may send a redundant packet via an alternate path, but only if it is a source of a packet. Experimental results show that EARQ is suitable for industrial applications, due to its capability for energy efficient, real-time, reliable communications.

Recently, industrial wireless sensor networks have attracted more attention, due to their large benefits in terms of faster installation, cost savings, and flexibility. Nevertheless, the acceptance of wireless sensor networks by the industrial community is not without its difficulties. In fact, several research efforts have been made in this field and a number of state-of-the-art reviews exist, presenting the current standards, the challenges, and the design principles. However, a comprehensive review on routing protocols appears to be missing. In this paper, we give a survey on routing protocols for industrial monitoring applications of the wireless sensor networks technology, and we present their limitations and weaknesses according to the industrial requirements.

In this paper, we propose a multi-agent systems approach for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on the development of a set of simulation models to support our distributed management approach for tracking mobile nodes in a large-scale industrial wireless sensor network. Our simulation models build on models and concepts from the literature on wireless sensor networks and wireless communication, with adaptations to address the needs of an industrial environment. An example of mobile node tracking with our distributed management approach is presented, showing accurate tracking performance for both fixed and random mobile node paths.

As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively.

With the success of wireless technologies and the number of wireless devices increasing, the wireless coverage footprint expands and removes the needs of wired networks for industrial applications. Wireless industrial sensor networks (WISNs) are a type of wireless sensor networks (WSNs), which dedicate to industrial applications. The WISNs bring several advantages over traditional wired industrial networks for detection and control, include flexibility, scalability, remote maintenance, and rapid deployment. However, some technical issues and routing design principles need to be noticed in terms of the stringent requirements of industrial applications. In this paper, we firstly introduce the characteristics and some routing issues of WISNs. Then we discuss existing routing protocols for WISNs and analysis their performances. Finally, we outline several future research directions.

This paper proposes PriorityMAC, a priority-enhanced medium access control protocol, designed for critical traffic in industrial wireless sensor and actuator networks (IWSAN). A notable trend in industrial automation in recent years has been the replacement of wired communication by IWSANs. Exceeding the required delay bound for unpredictable and emergency traffic could lead to system instability, economic and material losses, system failure, and, ultimately, a threat to human safety. Guaranteeing the timely delivery of the IWSAN critical traffic and its prioritization over regular traffic (e.g., noncritical monitoring traffic) is a significant challenge. Therefore, we present the design, implementation, performance analysis, and evaluation of PriorityMAC. A series of novel mechanisms (e.g., high priority indication space) are proposed to enable high-priority traffic to hijack the transmission bandwidth of the low-priority traffic. To the best of our knowledge, this is the first priority-enhanced MAC protocol compatible with industrial standards for IWSAN. PriorityMAC is implemented in TinyOS and evaluated on a testbed of Telosb motes. The experimental results indicate that PriorityMAC efficiently handles different traffic categories with different latency requirements, thereby achieving a significant improvement in the delivery latency compared with the current industrial standards.

The integration of Wireless Sensor Networks (WSNs) in Industrial Internet of Things (IIoT) applications presents significant challenges in terms of energy efficiency and network reliability, especially in dynamic industrial environments. The Routing Protocol for Low-Power and High-Loss Networks for Indoor Environments (RPLIE), while designed for low-power lossy networks (LLNs), lacks mechanisms to adequately balance energy consumption, a critical requirement for industrial sustainability. This research introduces an enhancement called Energy-Conscious Routing Protocol for Industrial Environments (EC-RPLIE), which incorporates the Expected Breakage Cost (EBC) metric to optimize energy distribution and network stability by managing medium-term jitter. Through extensive simulations in the Cooja environment, the performance of EC-RPLIE was evaluated against the state-of-the-art RPLIE across topologies of 11, 21, and 31 nodes. Quantitative results demonstrate that EC-RPLIE significantly reduces unnecessary retransmissions by maintaining a superior Packet Delivery Ratio (PDR) and optimizing parent selection. The protocol achieved energy savings of 9.6% in 11-node networks, which increased to 36.8% in high-density 31-node scenarios, effectively doubling the network persistence compared to RPLIE. Additionally, EC-RPLIE improved average latency by 12.68% in dense configurations, confirming its robustness in handling industrial traffic. These findings confirm that EC-RPLIE is particularly effective in high-density networks, where the EBC metric successfully mitigates the 'retransmission storms' typical of standard protocols. This proposal provides a robust framework for enhancing the sustainability and resilience of WSNs in Industry 4.0, offering a scalable solution that addresses the energy-reliability trade-off. The results lay the groundwork for future large-scale implementations in real-world industrial environments.

The industrial wireless sensor network (IWSN) is a surface-type of wireless sensor network (WSN) that suffers from high levels of security breaches and energy consumption. In modern complex industrial plants, it is essential to maintain the security, energy efficiency, and green sustainability of the network. In an IWSN, sensors are connected to the Internet in a non-monitored environment. Hence, non-authorized sensors can retrieve information from the IWSN. Therefore, to ensure that data access between sensors remains sustainable and secure, energy-efficient authentication and authorization are required. In this article, a novel Quantum Readout Gradient Secured Deep Learning (QR-GSDL) model is proposed to ensure that only trustworthy sensors can access IWSN data. The major objective of this QR-GSDL model is to create secure, energy-efficient IWSN to attain green sustainability and reduce the industrial impact on the environment. First, using the quantum readout and hash function, a registration method is designed to efficiently perform the registration process. Next, a gradient secured deep learning method is adopted to implement the authentication and authorization process in order to ensure energy-saving and secure data access. Simulations are conducted to evaluate the QR-GSDL model and compare its performance with that of three well-known models: online threshold anomaly detection, machine learning-based anomaly detection, and dynamic CNN. The simulation outcomes show that the proposed model is secure and energy-efficient for use in the IWSN. Moreover, the experimental results prove that the QR-SGDL model outperforms the existing models in terms of energy consumption, authentication rate, authentication time, and false acceptance rate.

Tracking mobile nodes in dynamic and noisy conditions of industrial environments has provided a paradigm for many issues inherent in the area of distributed control systems in general and wireless sensor networks in particular. Due to the dynamic nature of the industrial environments, a practical tracking system is required that is adaptable to the changes in the environment. More specifically, given the limited resources of wireless nodes and the challenges created by harsh industrial environments there is a need for a technique that can modify the configuration of the system on the fly as new wireless nodes are added to the network and obsolete ones are removed. To address these issues, two cluster-based tracking systems, one static and the other dynamic, are proposed to organize the overall network field into a set of tracking zones, each composed of a sink node and a set of corresponding anchor nodes. To manage the wireless nodes activities and inter and intra cluster communications, an agent-based technique is employed. To compare the architectures, we report on a set of experiments performed in JADE (Java Agent Development Environment). In these experiments, we compare two agent-based approaches (dynamic and static) for managing clusters of wireless sensor nodes in a distributed tracking system. The experimental results corroborate the efficiency of the static clusters versus the robustness and effectiveness of the dynamic clusters.

The performance of wireless sensor networks (WSNs) in industrial applications may be degraded due to harsh radio signal propagation conditions. Mobile heavy machines and large metal and concrete surfaces usually worsen the signal fading effects resulting in deep fades. We have studied the effects of certain industrial environmental disturbances on the quality of the transmitted signals in real-life industrial environments. We used software defined radio (SDR) as the receiver to capture the transmitted signals and analyzed the statistical properties of the captured signals with Matlab/Simulink tools. Our studies led us to a method which aims to identify the transmission problems related to signal multipath propagation by calculating the probability density function (PDF) of the received signal and comparing it to the simulated theoretical Rician distributions. The results show that tracking the transformations of the PDF of the received signal helps to recognize the temporal changes in radio link environment and in that way can help to choose the right actions in order to overcome the communication problems.

Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (m,k)-firm model. The proposed model processes captured information by employing a multimedia stream in the (m,k)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (m,k)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (m,k)-firm model regardless of the amount of traffic.

Industrial Wireless Sensor Networks (IWSNs) integrate various types of sensors to measure and control industrial production. However, the unattended open environment makes IWSNs vulnerable to malicious attacks, such as Sybil attacks, which may degrade the network performance. In addition, multipath distortion, impulse noise and interference effects in the harsh industrial environment may influence the accuracy of attack detection. In this paper, we propose a Sybil detection scheme based on power gain and delay spread analysis by exploiting the spatial variability from their channel responses. Specifically, we utilize channel-vectors to represent the sensor features based on the power gain and delay spread extracted from channel response. Furthermore, we develop a kernel-oriented method to distinguish Sybil attackers from benign sensors by clustering the channel-vectors. In addition, to alleviate the impact of industrial noise and interference effects, we design a multi-kernel based fuzzy c-means method to map the extracted channel-vectors into a new feature space such that the dispersive effects on the channel-vectors can be reduced. We also propose a parameter selection method to optimize the employed kernels. The simulation results show that the proposed multi-kernel scheme can achieve high accuracy in detecting the packets from Sybil attackers, and tolerate the dispersive attenuation and interference effects in the industrial environments.