Modeling and Analyzing Urban Sensor Network Connectivity Based on Open Data

SOHCL-RDT: A self-organized hybrid cross-layer design for reliable data transmission in wireless network

Classification of sensory data is a major research problem in wireless sensor networks and it can be widely used in reducing the data transmission in wireless sensor networks effectively and also in process monitoring. In order to examine the huge size of data set in stream model generated by sensor network, it will be analyzed different sensor's output signal, topology of sensors network, number of sensor parameters and number of acquisition data. In our wind energy monitoring, sensor node monitors six attributes: speed, direction, temperature, pressure, humidity, and battery voltage. Every attribute value is set as four measures: average, instantaneous, minimum, and maximum. This paper presents several data mining techniques applied on the wireless sensor network's data considered: Naïve Bayes, k-nearest neighbor, decision trees, IF-THEN rules, and neural networks. Before classification, the data was clustered in order to be labeled. A similarity based algorithm, k-means, was selected in the clustering process for its simplicity and efficiency. A conclusion that decision trees are a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation.

This paper presents an approach of noise-immune data transmission in wireless sensor networks using Frequency-Hopping Spread Spectrum. Redundancy of Residue Number System codes is explored, and adaptive errors correction system is proposed without code algorithm changing by reduction of information modules number or increasing of reference modules number.

The nodes in wireless sensor networks (WSNs) are responsible for communicating data which is primarily of three types viz. video, audio and text. In literature, a large number of energy aware and shortest path based route selection approaches have been proposed to enhance the routing efficiency in WSNs. Most of these route selection approaches are designed by assuming fixed type and size of data packets and results in the same path selection for all types of data. This path selection, does not consider data size and data priority, results in high contention losses, topology failures and non-uniformity in energy depletion of nodes. In this paper, a route selection approach which is based on the preservation of network connectivity for improving overall network lifetime has been proposed for applications where sensor nodes are required to transmit different types of data. The proposed approach preserve highly connected edges at the initial rounds of data communication in an energy efficient manner, such that network connectivity would be maintained even at later rounds of data communications. The proposed route selection approach, VDR, (Variable Data rate Routing) has been simulated with both proactive and reactive route maintenance strategies. The simulation results show that the proposed algorithm with both proactive and reactive route maintenance strategies result in better network lifetime, energy consumption, overall network connectivity, packet delivery ratio as compared to the existing State-of-the-Art energy efficient route selection approaches with proactive and reactive route maintenance strategies. The results also show that the proposed algorithm is scalable and performs better than the compared algorithms both at low node density and high node density.

For Wireless Multimedia Sensor Networks (WMSN), how to increase the throughput in constrained environments has become the main challenge. Multipath routing is a promising solution because it can improve the channel utilization rate, reduce transmission delay and balance the transmission load. To pursue high efficiency for a multipath routing scheme, we need to take the characteristics of wireless environments and sensor networks into careful considerations. Based on such considerations, this paper introduces a new routing scheme which divides the whole network topology into many districts and simultaneously forwards data through these districts without interfering with each other - to achieve interference-free transmissions. Our scheme will adjust the load of each district according to the energy status of their nodes; it does not establish fixed paths in advance and will hence maintain high performance even when the topology changes rapidly. Simulation results show that, when compared with related routing schemes, our scheme can achieve higher performance at real-time transmission and meanwhile distribute transmission loads more evenly to most of the nodes in the network.

Research institutes and companies are focusing intensively on Complex Event Processing, Event Stream Processing, Information Flow Processing and Surprise Event Detection methods and algorithms. This phenomenon is intensified by the rapid evolution and spreading in practice of the Internet of Things services. A huge amount of data is created continuously by the physical and logical sensors embedded into various equipment and aimed to monitor the state of physical processes. Time critical query-response mechanisms of the QoS based systems are more and more penetrating in the low bandwidth wireless sensor environment, as well. The behavior of the wireless sensor network requires special evaluation of the state information sent to and processed by the sink node. The items of the status information flow are affected by the low energy and low bandwidth characteristics of sensor networks. Despite of uncertainty of the data transmission in wireless sensor networks the energy constraint permits rarely retransmission of data affected by error. To mitigate the aforementioned issues, cognitive infocommunication algorithms may prove to be an effective solutions. Consequently, this paper presents a method to detect special events and surprises in time processes of the sensor network communication. The proposed method is applied to ZigBee sensor network (based on IEEE 802.15.4 standard) in different congestion scenarios.

Wireless sensor networks for industrial communication require high reliability and low latency. Since state of the art wireless sensor networks do not entirely meet these requirements, novel system approaches need to be developed. Ultra wideband communication systems seem to be a promising approach due to the high bandwidth and the resulting robustness against frequency selectivity. Therefore, this paper analyzes the IEEE 802.15.4a impulse-radio ultra-wideband physical layer for the application in wireless sensor networks. The packet error rate for two types of receiver - energy detector and correlation receiver - is evaluated with respect to signal-to-noise ratio. Taking into account regulatory limitations and realistic transceiver characteristics, the maximal achievable coverage range is derived. As a result, the dependency between required reliability of a wireless sensor transmission and the achievable operating distance is given. This upper bound can be used as decision guideline in order to select a suitable system for deployment of a wireless sensor network.

The requirements to transceivers, which are intended for multimedia data transmission in wireless sensor networks, and existing system characteristics are discussed. Ultrawideband wireless systems based on direct chaotic data transmission are analyzed to reveal the prospects of their application to multimedia sensor networks. The results obtained during experimental studies into video data-flow transfer, which are performed in the point-to-point mode with the use of radio relaying and through-wall transmission, are presented.

The wireless sensors which are used in IoT applications are battery powered, which imposes extreme energy constraints on their operations. Therefore, a fundamental challenge lies in designing a sensor network which can maximize its lifetime. To address this issue in WSNs, data aggregation techniques have come up as a novel approach that minimizes the number of transmissions in the network, which in turn can optimize the network and increase its lifetime. In this paper, we have discussed a simple hierarchical data aggregation method for energy optimized data transmission in wireless sensor networks.

The article presents the development of algorithms for data transmission in wireless sensor networks based on fully homomorphic encryption (FHE) using a symmetric encryption algorithm Kuznyechik. During the study, the main points related to the use of modern sensor networks were considered: data transfer protocols, the most common vulnerabilities and various emulators of sensor networks. Approaches to the use of homomorphic encryption for data exchange are considered. An algorithm for the interaction of the base station and sensor nodes of the sensor network for data exchange based on FHE using the Kuznyechik algorithm has been developed. To use the encryption algorithm Kuznyechik as part of the FHE, an algorithm for constructing Zhegalkin polynomials for nonlinear cipher elements has been developed and implemented. For the encryption algorithm Kuznyechik, for the first time, Zhegalkin polynomials are obtained, which describe the operation of the replacement S-block. The use of the obtained Zhegalkin polynomials makes it possible to decrypt the data encrypted by the Kuznyechik algorithm and at the same time leave them in the encrypted form of the FHE.

Clustering is an effective approach to hierarchically organizing network topology and to prolong the lifetime of a wireless sensor network. Current clustering protocols usually utilize two techniques: selecting cluster heads with more residual energy and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster. Most of the researches in this field has focused on energy-efficient solutions, but caring less about the communication between Cluster Head (CH) nodes and Base Station (BS). When the sensor field is far away from the base station, the cluster heads are burdened with heavier relay traffic and tend to die much faster. To mitigate the problem, we propose an Energy-Efficient Hierarchical Routing Protocol (EEHRP) for long range transmission in the wireless sensor networks. It uses a number of gateway nodes, which do not engage in clustering, to connect the CHs and base station. They are responsible for transmitting packets received from the CHs to the base station, thus the CHs can preserve some energy in data forwarding and the gateway nodes can ease their burden by not participating in clustering. Simulation results show that EEHRP obviously increases the network lifetime and well balances the energy consumption among the sensor nodes.

The nature of multi-hop data transmission in wireless sensor network will cause serious load unbalance which will produce great restrains in related applications considering the limited energy resource. Relative load balance algorithms are usually performed inside the clusters without considering about the energy consumption of the whole network. A cluster-based balanced energy consumption algorithm (BECA) is proposed by introducing in multiple inter-cluster links to distribute the load, so as to achieve global load balance. Moreover, an efficient data collecting mechanism is proposed based on BECA to improve the traffic balance further. Simulating results based on NS2 show that BECA can obtain better balance properties and prolong the network lifetime effectively.

WSN (Wireless Sensor Network) is a network of devices which can transfer the data collected from an examined field via wireless links. Thus secure data transmission is required for accurate transfer of data from source to destination as data passes through various intermediate nodes. The study intends to perform shortest, secure path routing on the basis of trust through novel Hybridized Crow Whale Optimization (H-CWO) and QoS based bipartite Coverage Routing (QOS-CR) as well as to analyze the system’s performance. Nodes are randomly deployed in the network area. Initially, a trust metric formation is implemented via novel H-CWO and the authenticated nodes are selected. Then through the secure routing protocol, Cluster head (CH) is selected to perform clustering. Neighbourhood hop prediction is executed to determine the shortest path routing and secure data transfer is performed through novel QOS-CR. The proposed system is analyzed by comparing it with various existing methods in terms of delay, throughput, energy and alive nodes. The results attained from comparative analysis revealed the efficiency of the proposed system. The proposed novel H-CWO and QOS-CR exhibited minimum delay, high throughput, energy and maximum alive nodes thereby ensuring safe transmission of data from source node to destination node.

Wireless Sensor Network is the remote accessible network or the implementation of the network in the unattainable area to gather data from sensor nodes. The data from the network in wireless mesh topology is less secured based on the security features implemented. Any communication between the nodes are through the other nodes in the network. These motes in the network are restricted to memory and energy issues. Algorithms like symmetric key methods Ron Rivest, Adi Shamir and Leonard Adleman Algorithm (RSA) and Advanced Encryption Standard (AES) are used. It has less efficiency in terms of computation and memory size. It is also harder to implement in the Hop by hop protocol where the authenticity of the message need to pass from one node to the other with limited memory and energy where the need for security is high. So for better effective security and authentication we go for the Elliptic curve cryptography(ECC) algorithm. This method is more efficient with the key size and also more secure in terms. It is less vulnerable to security threat attacks. Modified effective implementing enhanced ECC with a features like Elliptic Curve Digital Signature Algorithm (ECDSA) in to add more secure and effective data transfer along with the key cipher of the text information.

ABSTRACTThe exponential growth of Internet of Things (IoT) devices has intensified the demand for efficient data transmission in wireless sensor networks (WSNs), particularly in 5G and beyond technology. This study introduces DeepCuckoo, a novel hybrid framework that synergizes deep learning with bioinspired optimization to enhance energy‐efficient cluster head (CH) selection in WSNs. Leveraging deep belief networks (DBNs) for unsupervised clustering, the framework classifies sensor nodes based on energy levels and proximity. Concurrently, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) dynamically evaluate residual energy, distance, and node capacity to optimize CH selection. Further refinement is achieved through the cuckoo search (CS) algorithm, which optimizes routing paths to minimize energy consumption and balance the network load. Simulation results demonstrate that DeepCuckoo achieves up to 55% energy savings, significantly outperforming existing methods such as EPOA‐CHS and E‐CERP. The framework also extends the network lifetime by 36% and reduces the latency by 50%, ensuring reliable data transmission in dynamic environments. By integrating adaptive learning with bioinspired optimization, this study advances the scalability and resilience of WSNs, offering a robust solution for future IoT ecosystems.