Processing In Wireless Sensor Networks Research Articles

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

The localization process in Wireless Sensor Networks (WSNs) is carried out for finding the exact position of the sensor node. WSN are applied in various number of applications like monitoring, humidity, hospitality and so on. Hundreds to thousands of nodes in the sensor sense the data to the base station. Thousands of nodes in the WSN makes the installing of Global processing System (GPS). It is more costly and the GPS not provide the exact location in the indoor environment. In spite of dense network, manual configuration is not possible in the indoor environment. This cause a raise in problem to identify the exact position without GPS or any manual configuration methods. This paper presents the methods to overcome the above issues in WSN localization, by path planning, metaheuristic and deep learning are the techniques having high scopes in the industries, mining etc. Many times sensing of data are restricted by accuracy, energy, power and efficiency in the nodes. This paper discuss the WSN localization process classifications and the methodologies in localization. Localization issues are minimized by various techniques like ToA, TDoA and RSSI. The types of localization, its applications are also discussed.

Enhancing Network Lifetime of Duty Cycle-Based WSN With Mobile Sink Using Ambient Energy Harvesting

In order to guarantee a successful data collection process in wireless sensor networks with mobile sinks (WSN-MS), two primary objectives must be reached: 1) enabling the mobile sink to retrieve the maximum amount of data and 2) making sure that the network operates as long as possible. The first problem has been solved previously by proposing an innovative solution HXMAC. To address the second problem, on which this paper focuses, ambient energy harvesting is used to continuously supply power to each sensor node. Thus, this paper's main contribution is to propose EH-HXMAC (HXMAC with energy harvesting), which is based on all these improvements: seamless handover, duty cycling optimization, and mainly energy harvesting capability. EH-HXMAC has been evaluated using Cooja Contiki simulator. Obtained results based on the evaluation of the proposal EH-HXMAC clearly show its suitability as a good solution to promote unlimited lifetime for WSN-MS.

PSO-based DV-Hop Localization for Healthcare Sensor Nodes

Locating specific sensor nodes can be a difficult process in wireless sensor networks, making it one of the most difficult aspects of these networks. It is helpful in pinpointing the precise location where something took place. As a result of the fact that this is a one-time process, nodes are often only localised once for the course of the network entire existence. On the other hand, a landslip or some other catastrophe that takes place in the real world can lead the nodes of the network to become distributed across it. When something comparable does place, it is absolutely necessary to be aware of the locations of these sensor nodes. In this paper, we suggest a different localization technique, one that is able to function with sensor nodes that have been moved. The system that has been proposed takes into account DV-Hop localization methods that are based on particle swarm optimization (PSO). We also take into consideration the electromagnetic abnormality model as an illustration of how the strategy that was described could be implemented in an anisotropic network. When compared to the conventional PSO DV-Hop structure, the simulations’ findings indicate that our innovative localization framework significantly reduces the amount of time, inaccuracy, and power consumption required.

A Hybrid Algorithm for Reliable and Energy-efficient Data Gathering in Wireless Sensor Networks

Reliability and energy efficiency are two important requirements of the data gathering process in wireless sensor networks. Accordingly, we propose a novel data gathering algorithm which meets these requirements. The proposed scheme categorizes the sensed data into valuable and normal data and handles each type of data based on its demands. The main requirement of valuable data is reliability. Thus, the adopted strategy to gather this type of data is to send several copies of data packets toward the sink. The rise of energy exhaustion in this scheme is tolerable. This is due to that, the valuable data is generated at a low rate. On the other hand, our main concern in gathering normal data is energy efficiency. As most of the sensed data is normal, an energy-efficient approach to gather normal data results in considerable energy conserving. Thus, we exploit clustering technique for normal data gathering. We also propose a lightweight intrusion detection system to detect malicious nodes. Simulation results and theoretical analysis confirm that our proposed algorithm provides reliability and energy efficiency at an acceptable level.

An Energy-efficient Data Collection Scheme by Mobile Element based on Markov Decision Process for Wireless Sensor Networks

An Energy-efficient Data Collection Scheme by Mobile Element based on Markov Decision Process for Wireless Sensor Networks

Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.

Energy-Efficient and Degree-Distance Clustering Based Hierarchical Routing Protocol for WSNs

Background: Wireless Sensor Networks (WSN) are of crucial importance in today's applications and are very useful in creating an intelligent environment. The characteristics of WSN such as the increasingly smaller size of the sensors, their lower cost, and wireless communication support etc., allow the sensor networks to quickly invade several areas of application and have made them a very active field of research. They incorporate an efficient technique that guarantees an optimization and a better distribution of the energy resource. The sensors collaborate to carry out specific tasks according to the objectives of the application. Objective: This paper uses degree distance clustering for optimizing routing process in wireless sensor networks. Methods: Our protocol aims to more equitably exploit the energy of the selected nodes clusterheads, and to save energy dissipated while routing the captured data to the base station. Results: The results of the simulations show that our proposed protocol allows a reduction in energy dissipation and duration life of the larger network. Conclusion: Hierarchical routing in WSNs can be optimized when clustering technique is efficiently established.

Design and implementation of a cloud-based event-driven architecture for real-time data processing in wireless sensor networks

The growth of the Internet of Things (IoTs) and the number of connected devices is driven by emerging applications and business models. One common aim is to provide systems able to synchronize these devices, handle the big amount of daily generated data and meet business demands. This paper proposes a cost-effective cloud-based architecture using an event-driven backbone to process many applications’ data in real-time, called REDA. It supports the Amazon Web Service (AWS) IoT core, and it opens the door as a free software-based implementation. Measured data from several wireless sensor nodes are transmitted to the cloud running application through the lightweight publisher/subscriber messaging transport protocol, MQTT. The real-time stream processing platform, Apache Kafka, is used as a message broker to receive data from the producer and forward it to the correspondent consumer. Micro-services design patterns, as an event consumer, are implemented with Java spring and managed with Apache Maven to avoid the monolithic applications’ problem. The Apache Kafka cluster co-located with Zookeeper is deployed over three availability zones and optimized for high throughput and low latency. To guarantee no message loss and to simulate the system performances, different load tests are carried out. The proposed architecture is reliable in stress cases and can handle records goes to 8000 messages in a second with low latency in a cheap hosted and configured architecture.

Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism for improving resilience in wireless sensor networks

SummaryThe clustering process in wireless sensor networks is considered to be predominant for facilitating minimized energy consumptions through the selection of potential cluster heads that are efficient in energy and trust. The significant cluster head selection is deterministic in topology control with the view to manage the sensor member nodes of the cluster with the view to enhance energy efficiency in the network. In this paper, a Markov Modulated Bernoulli Prediction Process‐based Cluster Head Selection Mechanism (MMBPP‐CHSM) is proposed by modeling the success probability of sensor nodes' state into a Bernoulli process that dynamically evolves over time based on Markov chain for extending the lifetime of the network. This proposed MMBPP‐CHSM scheme is vital in quantifying the reliability and availability of the sensor nodes in a dynamically evolving wireless sensor network through the estimation of trust and energy in order to facilitate effective cluster head selection. The simulation results of the proposed MMBPP‐CHSM scheme confirmed a superior enhancement in percentage of alive nodes and death nodes in the network to a maximum level of 28% and 23% on par with the baseline cluster head selection approaches used for investigation.

Shadow: A Lightweight Block Cipher for IoT Nodes

The advancement of the Internet of Things (IoT) has promoted the rapid development of low-power and multifunctional sensors. However, it is seriously significant to ensure the security of data transmission of these nodes. Meanwhile, sensor nodes have the characteristics of converting analog signals into digital signals for operation processing in wireless sensor networks (WSNs). Given the particularity of Addition or AND, Rotation, and XOR (ARX) operations, its round function can only be based on the Feistel structure or generalized Feistel structure, otherwise, the process of decryption cannot be completed correctly. Furthermore, the existing ARX ciphers have the problems of only changing half of the plaintext block in one round and iterating for many rounds. In this article, a new logical combination method of generalized Feistel structure and ARX operations is proposed to improve the diffusion speed of ARX ciphers, called Shadow. Shadow overcomes the shortcomings of traditional ARX ciphers that only diffuse half of the block in one round. To ensure the efficiency of the encryption hardware circuit while ensuring the security of the physical-layer signal, we studied the round-based hardware architecture and the serial hardware architecture for Shadow cipher. Particularly, we conducted a series of performance tests on Shadow, including the avalanche effect, FPGA implementation, and ASIC implementation. Also, we conducted a security analysis of the Shadow. As shown by our experiments and comparisons, Shadow is compact in IoT nodes and is of high security against cryptanalysis.

Towards Designing a Trusted Routing Scheme in Wireless Sensor Networks: A New Deep Blockchain Approach

Routing is a critical process in Wireless Sensor Networks (WSNs) since it is responsible for data transmission to base stations. Routing attacks are capable of completely destroying and degrading the function of WSNs. A trustworthy routing system is critical for ensuring routing security and WSN efficiency. Numerous studies have been conducted to increase trust between routing nodes, including cryptographic techniques, and centralized routing decisions. Nonetheless, the majority of routing methods are impractical in practice, since it is difficult to identify untrusted activities of routing nodes effectively. Meanwhile, there is no efficient method of preventing malicious node attacks. As a result of these issues, this article offers a trusted routing method that combines deep blockchain and Markov Decision Processes (MDPs) in order to enhance the routing security and efficiency of WSNs. To authenticate the process of transmitting the node, the proposed approach utilizes a Proof of Authority (PoA) method inside the blockchain network. The validation group necessary for proofing is selected using a deep learning methodology that focuses on the properties of each node. MDPs are then used to choose the appropriate next hop as a forwarding node capable of transferring messages simply and securely. According to testing data, our routing system still performs well in a 50% malicious node routing environment when compared to existing routing algorithms.

An improved cluster formation process in wireless sensor network to decrease energy consumption

Wireless sensor network has special features and many applications, which have attracted attention of many scientists. High energy consumption of these networks, as a drawback, can be reduced by a hierarchical routing algorithm. The proposed algorithm is based on the Low Energy Adaptive Clustering Hierarchy (LEACH) and Quadrant Cluster based LEACH (Q-LEACH) protocols. To reduce energy consumption and provide a more appropriate coverage, the network was divided into several regions and clusters were formed within each region. In selecting the cluster head (CH) in each round, the amount of residual energy and the distance from the center of each node were calculated by the base station (including the location and residual energy of each node) for all living nodes in each region. In this regard, the node with the largest value had the highest priority to be selected as the CH in each network region. The base station calculates the CH due to the lack of energy constraints and is also responsible for informing it throughout the network, which reduces the load consumption and tasks of nodes in the network. The information transfer steps in this protocol are similar to the LEACH protocol stages. To better evaluate the results, the proposed method was implemented with LEACH LEACH-SWDN, and Q-LEACH protocols using MATLAB software. The results showed better performance of the proposed method in network lifetime, first node death time, and the last node death time.

Research on Attack and Defense Game Model for Improving the Efficiency of Network Intrusion Detection System

The widespread use of wireless networks has expanded people’s ability to obtain information, but its inherent network characteristics make it more vulnerable to cyber-attacks. Existing intrusion detection systems usually only target specific attack methods, but are incapable of other attacks. Based on this research background, taking game theory as the theoretical basis, it analyses the offensive and defensive process in wireless sensor networks, and demonstrates the necessity of implementing an intrusion detection system by analysing model equilibrium solutions. In view of the diversity of attack methods of network intruders, the game model is further improved, and a non-cooperative complete information static game model is established. By analysing the mixed Nash equilibrium solution of the model, the best defines strategy of the intrusion detection system is obtained and the system is balanced. Energy consumption and detection efficiency. Simulation experiment results show that the wireless sensor network intrusion detection system based on offensive and defensive game can not only effectively resist multiple network attacks, but also reduce the energy consumption caused by the intrusion detection system and prolong the service life of the network.

An Event-based Local Action Paradigm to Improve Energy Efficiency in Queriable Wireless Sensor Actuator Networks

Wireless sensor networks (WSN) are deployed in a multitude of applications both in industrial and academic fields. In recent years, due to the emerge of Internet of Things (IoT) technologies and Vehicle2X communication scenarios, novel challenges for wireless sensor network platforms - regarding hardware and software - arose. Thus, challenges known from big data processing have reached the WSN scope and consequently approaches and methods have been devised to handle these. One such approach is queriable wireless sensor networks which enable their users the specification of sensing tasks in a declarative way without the need to re-program nodes in case the application requirements change. As many current WSN applications feature active parts with which nodes can directly influence their environment, the term wireless sensor actuator networks (WSAN) has been coined, setting such networks apart from solely passively measuring networks.In this article, we will present a short introduction to big data processing in wireless sensor networks which motivates the usage of queriable networks. We will show that in order to enable a WSAN to carry out actions energy-efficiently and in a timely manner, an event-based action model is favorable. Additionally, we will demonstrate how such an event system can be used to improve sub query performance in WSNs. We conclude with an evaluation regarding the benefit of combining this approach with wake-up receiver technologies based on a qualitative energy efficiency definition for WSN.

Geographic Routing In WSN for Measuring Coverage Constraints and Energy Consumption in Cloud Environments

This paper investigates the problem of energy efficient and consumption process in wireless sensor networks. The fundamental problem in WSN has handled the energy constraints and avoiding end-to-end delay networks. We introduce encoding technique for handling transmission data, network delays, content aware service and energy expenditure problems. The end-to-end delay varies depending up on nodes and sensor life time. We apply hamming encoding technique for estimating energy constraints with life time of buffer capacity factor. This paper proposes the novel based approach to handle group communication, senor node status, network transmission and optimal encoded behaviors. Each cluster group values are sensed by node reference time and calculate hamming code weight for each node counts. The quality of services can be achieved by energy saving measurements and data loss can be varies up on incoming packet request. We compare the encoded result with code word situation and hazard environment. We reduce the amount of data transmission factor and life time of sensor values and longer network delays. We show the simulating results with encoding scheme and reduced energy consumption. The performance can be monitored by coverage constraints and optimal transmission behaviors.

Prediction of time series using wavelet Gaussian process for wireless sensor networks

The detection and transmission of a physical variable over time, by a node of a sensor network to its sink node, represents a significant communication overload and consequently one of the main energy consumption processes. In this article we present an algorithm for the prediction of time series, with which it is expected to reduce the energy consumption of a sensor network, by reducing the number of transmissions when reporting to the sink node only when the prediction of the sensed value differs in certain magnitude, to the actual sensed value. For this end, the proposed algorithm combines a wavelet multiresolution transform with robust prediction using Gaussian process. The data is processed in wavelet domain, taking advantage of the transform ability to capture geometric information and decomposition in more simple signals or subbands. Subsequently, the decomposed signal is approximated by Gaussian process one for each subband of the wavelet, in this manner the Gaussian process is given to learn a much simple signal. Once the process is trained, it is ready to make predictions. We compare our method with pure Gaussian process prediction showing that the proposed method reduces the prediction error and is improves large horizons predictions, thus reducing the energy consumption of the sensor network.

Modeling and Analysis of Communication Subsystem design Process for Wireless Sensor Networks based on Petri Net

Wireless Sensor Networks (WSNs) have numerous applications in the field of engineering. These networks have variety of design constraints and energy is most important one. As energy harvesting is not possible in most of the cases, energy conservation is the only option available to the researchers. Energy efficient communication is the key to energy conservation as most of the energy is spent in transfer of data from one node to another. This article is devoted to development of energy efficient communication system design process model based on Petri Net for Wireless Sensor Networks. This model not only facilitates to examine the dynamicity in design process but also evaluates the design process for any deadlock conditions. Based on this model, radiating element array for communication in sensor networks can be designed and developed and therefore can be used to improve the overall energy efficiency of the network.

Energy-efficient and privacy-preserving spatial range aggregation query processing in wireless sensor networks

The existing privacy-preserving aggregation query processing methods in sensor networks rely on pre-established network topology and require all nodes in the network to participate in query processing. Maintaining the topology results in a large amount of energy overhead, and in many cases, the user is interested only in the aggregated query results of some areas in the network, and thus, the participation of the entire network node is not necessary. Aiming to solve this problem, this article proposes a spatial range aggregation query algorithm for a dynamic sensor network with privacy protection (energy-efficient privacy-preserving data aggregation). The algorithm does not rely on the pre-established topology but considers only the query area that the user is interested in, abandoning all nodes to participate in distributing the query messages while gathering the sensory data in the query range. To protect node data privacy, Shamir’s secret sharing technology is used to prevent internal attackers from stealing the sensitive data of the surrounding nodes. The analysis and experimental results show that the proposed algorithm outperforms the existing algorithms in terms of energy and privacy protection.

Efficient Continuous Skyline Query Processing in Wireless Sensor Networks

Owing to the rapid advent of wireless technology and proliferation of smart sensors, wireless sensor networks (WSNs) have been widely used to monitor and query the physical world in many applications based on the Internet of Things (IoT), such as environmental monitoring and event surveillance. A WSN can be treated as a distributed database to respond to user queries. Skyline query, as one of the popular queries for multi-criteria decision making, has received considerable attention due to its numerous applications. In this paper, we study how to process a continuous skyline query over a sensor data stream in WSNs. We present an energy-efficient continuous skyline query method called EECS. EECS can avoid the transmission of invalid sensor data and prolong the lifetime of WSNs. Extensive experiments are conducted, and the experimental results demonstrate the effectiveness of the proposed method.

A study on Various Techniques handled for Data Aggregation Process in Wireless Sensor Network

A study on Various Techniques handled for Data Aggregation Process in Wireless Sensor Network