Real-time Wireless Sensor Networks Research Articles

Towards optimal path planning for sustainable data collection in real-time wireless sensor networks

ABSTRACT Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojourning at rendezvous points (RPs) and gathering data there from nearby sensor nodes (SNs). However, determining the optimal number and location of RPs is a hard problem. Metaheuristic algorithms are well suited to solve such kind of problems. In this paper, we propose a tour planning scheme for the MS by a combined approach that uses K-means clustering and Particle Swarm Optimization algorithm. The proposed scheme first determines a set of near-optimal RPs and then uses them to schedule the tour of the MS. Herein, the energy consumption of the overall scheme is reduced due to the low computational cost of the proposed algorithm. At last, the proposed scheme is shown to outperform the comparative algorithms through simulation.

Multigateway Designation for Real-Time TSCH Networks Using Spectral Clustering and Centrality

This letter proposes a multi-gateway designation framework to design real-time wireless sensor networks (WSNs) improving traffic schedulability, i.e., meeting the traffic time constraints. To this end, we resort to Spectral Clustering unsupervised learning that allows defining arbitrary k disjoint clusters without knowledge of the nodes physical position. In each cluster we use a centrality metric from social sciences to designate one gateway. This novel combination is applied to a time-synchronized channel-hopping (TSCH) WSN under earliest-deadline-first (EDF) scheduling and shortest-path routing. Simulation results under varying configurations show that our framework is able to produce WSN designs that greatly reduce the worst-case network demand. In a situation with 5 gateways, 99% schedulability can be achieved with 3.5 times more real-time flows than in a random benchmark.

IOT Based Bridge Collapse and Flood Detection System

Knowledge on the dynamic properties of bridges improves condition assessment, maintenance scheduling and emergency planning to better serve the public. The Crack inspection is an important task in the maintenance of bridge and it is closely related to structural health of the bridge. It is done through a very manual procedure, an experienced human inspector monitors the whole bridge visually and try to detect cracks on the bridge and marks the location of the crack. The proposed system is implemented using a real time wireless sensor network for bridge monitoring is of lossless data transmission over several minutes continuously. Overall information about the bridge will be stored on the cloud. The cracks will be detected automatically and send the real time data with location of crack to the PWD department. If bridge will be collapse because of any accident or a flood that time the barriers will be open and the Vehicles will stop at some distance, and also notify about the accident to the nearby government departments like Police station, Hospitals and Society. After the accident detected by system up to previous and next 5KM the alert notification will shows another route for travellers.

Recent Developments in Wireless Soil Moisture Sensing to Support Scientific Research and Agricultural Management

In recent years, wireless sensor network (WSN) technology has emerged as an important technique for wireless sensing of soil moisture from the field to the catchment scale. This review paper presents the current status of wireless sensor network (WSN) technology for distributed, near real-time sensing of soil moisture to investigate seasonal and event dynamics of soil moisture patterns. It is also discussed how WSN measurements of soil measurements contribute to the validation and downscaling of satellite data and non-invasive geophysical instruments as well as the validation of distributed hydrological models. Finally, future perspectives for WSN measurements of soil moisture are highlighted, which includes the improved integration of real-time WSN measurements with other information sources using the latest wireless communication techniques and cyberinfrastructures.

Time-Critical Data Transmission Scheme in Wireless Sensor Networks Using Machine Learning Approach

Wireless sensor network has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. In this paper, we propose a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work, and confirm the accuracy of the proposed MAC protocol strategy is higher than the existing work.

Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning

In the industrial environment, the data transmission of Wireless Sensor Networks (WSNs) usually has strict deadline requirements. Improving the reliability and real-time performance of data transmission has become one of the critical issues in WSNs research. One of the main methods to improve the network performance of WSNs is to schedule the transmission process. An effective scheduling algorithm can meet the requirements of a strict industrial environment for network performance, which is of great research significance. Aiming at the problem of concurrent data transmission in WSNs, a real-time data transmission scheduling algorithm based on deep Q-learning is proposed. The algorithm comprehensively considers the influence of the remaining deadline, remaining hops, and unassigned time-slot nodes in the data transmission process, defines the reward function and action selection strategy of Q-learning, and guides the system state information transfer process. At the same time, deep learning and Q-learning are combined to solve the problem of disaster maintenance caused by the large scale of the system state. A multi-layer Stacked Auto Encoder (SAE) network model establishes the state-action mapping relationship, and the Q-learning algorithm updates it. Finally, according to the trained SAE network model, the data transmission scheduling strategy of the system in different states is obtained. The network performance of the proposed data transmission scheduling algorithm is analyzed and evaluated by simulation experiments. The simulation results show that compared with the commonly used heuristic algorithms, the proposed algorithm improves real-time performance and can better meet the data transmission requirements of high reliability and real-time WSNs.

A Frequency-Time Synchronization Scheme for Real-Time Wireless Sensor Networks

In a real-time wireless sensor network (RT-WSN), an unpredictable time length of the synchronization (or connection) process between nodes is generally a pity, though the communication after the connection may be controllable. The purpose of this paper is to solve this kind of pity based on the multiple-request-single occasion (multiple slave nodes request to send data to a single master node simultaneously before getting synchronized using the frequency channel hopping technique). Suppose that the master sends the synchronization packet (or beacon) and the slaves scan for this packet with different channels for connection. A slave getting synchronized with the master means that both nodes have just selected an identical frequency channel during a time region and the slave has received the synchronization packet successfully in this region, which is called frequency and time synchronization, abbreviated as FTS. For many existing wireless protocols, if they are directly adopted in this situation, two deficiencies exist as for real-time performances: First, the time length required for a slave to join the network is often not deterministic if one or more channels are disturbed. Second, when multiple slaves do their scanning simultaneously, which slave can synchronize with the master first is unpredictable so that a slave with a lower priority may be serviced prior to others. In this paper, two FTS examples with poor real-time performances are provided first. Then, a synchronization method named 1/2n FTS is presented and proved. With this method, a slave scans for the synchronization packet of the master with n different available channels repeatedly until it gets the packet while the master transmits the packet 2n times in 2n continuous timeslots. The width of the scan widow of the salve takes twice the width of the slot. In this way, every slave has the opportunity to get synchronized with the master at the end of the 2n slots even if one or more (not all) channels are disturbed. Then, the slaves can send their requests to the master in different slots so that the master can schedule subsequent communications according to their requests and priorities. Also, if the mater broadcasts the beacon periodically, the time length for a slave to join or rejoin the master is not difficult to predict. The theorems associated with the 1/2n FTS method are demonstrated in experiments with NORDIC Semiconductor chips.

Real-time condition assessment of a painted megalithic cave using Wireless Sensor Network

The deterioration of underground heritage caves caused by visitations has attracted extensive attention over the recent decades. Most previous research focused on investigating the impact of visitors on the microclimatic conditions of the interior of large show caves, but much less relevant effort was made for small confined caves, for example, megalithic burial dolmen caves. In addition to environmental condition issues, the structural performance of underground heritage caves also deteriorates in the long-term subject to both natural hazards (e.g. creep in geo-materials and extreme weather) and manmade ones (e.g. construction activities). To this end, a real-time Wireless Sensor Network (WSN) system is deployed inside an underground megalithic dolmen to monitor its structural performance with time so as to assess its long-term structural stability. Meanwhile, the WSN system together with other field sensors and inspection monitors the interior environmental change caused by human visitation effect, aiming to reveal the mechanism behind visitor-caused rock painting deterioration in the small confined dolmen chamber. Results show that structurally, the dolmen can be regarded as generally stable despite a progressive development of structural performance during the first 1.5 months and accidental instability of the near-opening pillar A. In terms of environmental condition, the presence of visitors leads to significant changes of interior microclimatic conditions frequently inside the confined cave against consistent rock painting conservation. Particularly, the cave paintings may deteriorate critically with time as visitation induces a ‘hotbed’ environment for interactions between microorganisms and microstructures of the rock paintings due to the variations of interior microclimate. To ensure long-term structural stability and restore acceptable level of interior microclimate, tinted hydraulic lime and steel rod/brace were recommended for enhancing structural robustness and a mechanical ventilation system for improving environmental resilience of the dolmen. A generalized monitoring approach was proposed for the reference of other similar caves.

An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks

Artificial Intelligence-based Quorum systems are used to solve the energy crisis in real-time wireless sensor networks. They tend to improve the coverage, connectivity, latency, and lifespan of the networks where millions of sensor nodes need to be deployed in a smart grid system. The reality is that sensors may consume more power and reduce the lifetime of the network. This paper proposes a quorum-based grid system where the number of sensors in the quorum is increased without actually increasing quorums themselves, leading to improvements in throughput and latency by 14.23%. The proposed artificial intelligence scheme reduces the network latency due to an increase in time slots over conventional algorithms previously proposed. Secondly, energy consumption is reduced by weighted load balancing, improving the network's actual lifespan. Our experimental results show that the coverage rate is increased on an average of 11% over the conventional Coverage Contribution Area (CCA), Partial Coverage with Learning Automata (PCLA), and Probabilistic Coverage Protocol (PCP) protocols respectively.

Comparison of Q-Coverage P-Connectivity Sensor Node Scheduling Heuristic Between Battery Powered WSN & Energy Harvesting WSN

A serious problem in Wireless Sensor Networks (WSNs) is to attain high-energy efficiency as battery powers a node, which has limited stored energy. They can not be suitably replaced or recharged. The appearance of renewable energy harvesting techniques and their combination with sensors give Energy Harvesting Wireless Sensor Networks (EH-WSNs). Therefore, the area shifts from energy preservation to the reliability of the network. For reliability, Coverage and Connectivity are important Quality of Service (QoS) parameters. Many sensor node scheduling heuristics have been developed in the past. Some of them focus on more than a single order of Coverage and Connectivity. If reliability is the main concern for a WSN, then there definitely is a need to incorporate Q-Coverage and P-Connectivity in WSN. The lifetime of a WSN decreases while considering Q-Coverage and P-Connectivity. After some time network dies and it is of no use. Therefore, there is a trade-off between the reliability and lifetime of a WSN. If EH-WSN is used, the lifetime of a WSN increases, as well as Q-Coverage and P-Connectivity QoS parameters can be used for achieving reliability. This paper proposes a sensor node scheduling heuristic for EH-WSN, considering Q-Coverage and P-Connectivity. A comparison of a Q-Coverage and PConnectivity sensor node scheduling heuristic, when used between battery powered WSN and EH-WSN, is done. The comparison shows that lifetime using EH-WSN is much greater as compared to battery powered WSN. This research is beneficial for real-time WSN applications where EH-WSN provides power regularly without any intervention.

Effective Error Control Scheme in Real-Time Wireless Sensor Networks

Wireless Sensor Networks (WSNs) subsist on network of huge numbers of hubs that are distributed in unfriendly environments used for habitat monitoring and to observe changes in phenomena. These hubs are susceptible to faults such as energy exhaustion, hardware glitches, communication link errors, malicious attacks, which may lead to errors in message delivery. However, these systems are typically utilized to convey vital information in remote places. Thus, efficient error identification and correction is thereby required in order to maintain the accuracy of the received message. This paper aimed at designing high speed reverse converter for effective error control codes using Redundant Residue Number System in order to enhance the accuracy of messages delivered for real-time WSNs. In order to attain a greater error correction, a reverse converter which was based on Mixed Radix Conversion was designed for the moduli set {2n+1-1, 2n, 2n-1}. In addition to this moduli, an extra redundant moduli set{2n+1+3, 22n - 3} is also designed for higher correctness. For correction and identification of corrupted values in the original data, maximum likelihood was used. The results obtained show that the suggested moduli set offers low energy utilization and consistency of the received message in real time WSNs.

Evolutionary Computing Assisted Wireless Sensor Network Mining for QoS-Centric and Energy-efficient Routing Protocol

The exponential rise in wireless communication demands and allied applications have revitalized academia-industries to develop more efficient routing protocols. Wireless Sensor Network (WSN) being battery operated network, it often undergoes node death-causing pre-mature link outage, data drop and retransmission causing delay and energy exhaustion. Furthermore, the presence of a malicious node to impacts network performance adversely. In this paper, a highly robust and efficient Evolutionary computing assisted WSN routing protocol is developed for QoS and energy-efficiency. Our proposed routing protocol encompasses two key functions Network Condition Aware Node Profiling and Malicious Node Detection (NCAMND) exploits or mines the dynamic node/network parameters to identify malicious node, and Evolutionary Computing assisted Dual-Disjoint Forwarding Path (EC-DDFP) model learns over node/network connectivity and availability information to obtain a dual-disjoint path with no-shared components to ensure QoS centric and energy-efficient routing. Simulation results affirm that the proposed routing protocol achieves higher throughput, low energy consumption, and low delay that confirm its suitability for real-time WSN systems.

SPREAD - A Routing Protocol to Meet End-to-end Adjusted Deadline in Real Time Wireless Sensor Networks

Background: Real Time Wireless Sensor Networks (RT-WSN) have hard real time packet delivery requirements. Due to resource constraints of sensors, these networks need to trade-off energy and latency. Objective: In this paper, a routing protocol for RT-WSN named “SPREAD” has been proposed. The underlying idea is to reserve laxity by assuming tighter packet deadline than actual. This reserved laxity is used when no deadline-meeting next hop is available. Objective: As a result, if due to repeated transmissions, energy of nodes on shortest path is drained out, then time is still left to route the packet dynamically through other path without missing the deadline. Results: Congestion scenarios have been addressed by dynamically assessing 1-hop delays and avoiding traffic on congested paths. Conclusion: Through extensive simulations in Network Simulator NS2, it has been observed that SPREAD algorithm not only significantly reduces miss ratio as compared to other similar protocols but also keeps energy consumption under control. It also shows more resilience towards high data rate and tight deadlines than existing popular protocols.

Impact Analysis of Network Layer Attacks in Real-Time Wireless Sensor Network Testbed

With the rapid increase in the demand for Wireless Sensor Network (WSN) applications. The intrusive activities are also raised. To save these networks from the intruders it is required to understand the implications of any malicious act. Most of the researchers have utilized simulated software to understand the impact of such intrusions, however, real network conditions vary from the simulated environment. Therefore, the current work focuses on analyzing the impact of network layer attacks in real-time WSN testbed. The contributions of this work are threefold. Firstly, it presents the deployment of a real-time experimental testbed using standardized sensor devices in a multi-hop topological arrangement. Secondly, it provides the implementation details of seven network layer attacks: Blackhole (BH), Dropping Node (DN), Drop Route Request (DRREQ), Drop Route Reply (DRREP), Drop Route Error (DRERR), Grayhole (GH) and Sinkhole (SH) in a single testbed. Finally, the testbed performance with and without each attack is monitored and compared in terms of network performance metrics to understand the attacks’ impact. This work will be helpful for the research community for proposing efficient attack detection and prevention solutions for these networks.

Dynamic modulation scaling enabled multi-hop topology control for time critical wireless sensor networks

The previous work on connection driven topology control has shown that it has significant potential to reduce energy consumption of Wireless Sensor Networks (WSNs). Dynamic Modulation Scaling (DMS) which is a technique that manages transmission power levels in order to change the number of bits encoded per symbol has a direct impact on connection driven topology control. In this paper we investigate the transmission scheduling of multi-hop real-time WSNs equipped with DMS enabled radio chips while taking the effect of DMS on topology control into account. To our best knowledge, this is the first paper that addresses this issue. The current work on DMS enabled WSN tend to rely on theoretical DMS models to predict network performance metrics. However, there is little, if any, work that is based upon empirically verified network performance outcomes using DMS especially on its effect on connection driven topology control. This paper fills this gap by using GNU Radio and Software Defined Radio hardware to show how to emulate DMS in low power wireless systems and measure the impact of varying Signal-to-Noise levels, distance and elevation on throughput and delivery rates for different DMS control strategies. Next, we present the Mixed Integer Nonlinear Optimization Problem of minimizing energy consumption of DMS enabled connection driven topology control on real-time WSNs. Lastly, we present two polynomial time heuristics and compare their performance against the optimal solution.

Ant Colony Based Data Aggregation in Real Time Wireless Sensor Networks

Sensing the natural phenomenon, sensing the eventual activities, sensing the abrupt change in human developed systems has a wide number of applications. To sense, one needs the tiny sensor devices called sensor nodes. To collect the data from these devices, one has to arrange these tiny devices into a intelligently managed network, called as sensor networks. Due to mostly unattended application areas and zones, these devices are supposed to be wireless which adds one more dimension to our discussion, known as wireless sensor networks. So basically these are an intelligent arrangement of small, tiny sensing devices with wide array of attributes, namely, sensing ability, low communication range mechanism, limited power sources, small processing ability, small memory. With these limitations, even handling a small data generated/sensed in the network becomes crucial to handle. Therefore there has to be an effective data aggregation technique. The prime goal of any data aggregation algorithms is to collect/retrieve/gather the data in an efficient possible way so that the effort to communicate the data over the network can be reduced. Less work from the network means less energy dissipated, thus lifetime can be enhanced. An effective data aggregation technique reduces the latency also. Small network latency is crucial for time critical applications in real time wireless sensor networks. Ant colony based optimization provides us such a possibility to find an optimal route for efficient data aggregation with significantly reduced network latency. In our work, Ant colony based Data Aggregation in Real Time wireless sensor networks (ACDAR), Ants use their pheromones to establish a new path or follow already established path by other ants. Similarly data packets in wireless sensor networks can take advantage of freshly established paths by Route Ants. As the results received, it is verified that number of packets successfully delivered to the sink have increased and the miss ratio, cumulative packets consumption have improved, network latency has decreased.

Real Time Wireless Sensor Network (WSN) based Indoor Air Quality Monitoring System

This paper presents the development and implementation of a number of wirelessly connected sensor units to monitor indoor air quality parameters for real time visualisation and recording of the measured data. This is achieved by utilising infrared based sensor technology that measures carbon dioxide (CO2), temperature and humidity, low power wireless networking and Geo-statistic methods for spatial prediction. The platform consists an MBED LPC 1768 development board that retrieves data from the sensor unit and transmits it wirelessly using a ZigBee module to a central base station where it is analysed and stored. Spatial prediction is also performed in real time on live data. Initial measurements are performed in an office environment.

CapNet

As the scale and density of data centers continue to grow, cost-effective data center management (DCM) is becoming a significant challenge for enterprises hosting large-scale online and cloud services. Machines need to be monitored, and the scale of operations mandates an automated management with high reliability and real-time performance. The limitations of today’s typical DCM network are many-fold. Primarily, it is a fixed wired network, and hence scaling it for a large number of servers increases its cost. In addition, with server densities increasing over recent years, this network also has to be cabled correctly and the management of this network parallels the complexity of managing a data network, since it needs to be networked with multiple switches and routers. In this article, we propose a wireless sensor network as a cost-effective networking solution for DCM while satisfying the reliability and latency performance requirements of DCM. We have developed CapNet, a real-time wireless sensor network for power capping, a time-critical DCM function for power management in a cluster of servers. CapNet employs an efficient event-driven protocol that triggers data collection only on the detection of a potential power capping event. We deploy and evaluate CapNet in a data center. Using server power traces, our experimental results on a cluster of 480 servers inside the data center show that CapNet can meet the real-time requirements of power capping. CapNet demonstrates the feasibility and efficacy of wireless sensor networks for time-critical DCM applications.

A Modified Real-Time Fault-Tolerant Task Allocation Scheme for Wireless Sensor Networks

In WSNs, the sensor nodes are at risk of failure and malicious attacks (selective forwarding). This may have a profound negative effect when you consider real-time WSNs, making them challenging to deploy. When there is a delay in tasks allocation execution processes in real-time WSNs because of sensor nodes failures, this will cause disastrous consequences if the systems are safety-critical, e.g. aircraft, nuclear power plant, forest fire detection, battlefield monitoring, thus the need to developed a real-time system that is fault-tolerable. This paper developed a modified real-time fault-tolerant task allocation scheme (mRFTAS) for WSNs (wireless sensor networks), using active replication techniques. mRFTAS and RFTAS performance were compared using time of execution of the task, network lifetime and reliability cost. The mRFTAS performance showed an improvement over that of RFTAS when it comes to reducing the time it takes for task execution by 45.56% and reliability cost of 7.99% while prolonging the network lifetime by 36.35%.

Context-Sensitive Access in Industrial Internet of Things (IIoT) Healthcare Applications

Industrial Internet of Things (IIoTs) is the fast growing network of interconnected things that collects and exchange data using embedded sensors planted everywhere. Several IIoT applications such as the ones related to healthcare systems are expected to widely utilize the evolving 5G technology. This 5G-inspired IIoT paradigm in healthcare applications enables the users to interact with various types of sensors via secure wireless medical sensor networks (WMSNs). Users of 5G networks should interact with each other in a seamless secure manner. And thus, security richness is highly coveted for the real time wireless sensor network systems. Asking users to verify themselves before every interaction is a tedious, time-consuming process that disrupts inhabitants' activities, and degrades the overall healthcare system performance. To avoid such problems, we propose a context-sensitive seamless identity provisioning (CSIP) framework for the IIoT. CSIP proposes a secure mutual authentication approach using hash and global assertion value to prove that the proposed mechanism can achieve the major security goals of the WMSN in a short time period.