Trust Based Data Reduction in Sensor Driven Smart Environment

The emerging Internet of Things (IoT)-based Wireless Sensor Networks (WSN) consist of small size of sensor nodes for monitoring and collecting data from environmental conditions and it transmits to other sensors through the internet. The major issues in WSN are energy constraints that degrade the efficient functioning and lifetime of WSN. Therefore, a novel technique called Gradient Enhanced Broken-stick Regressive Multivariate Artificial Fish Swarm Optimized Data Collection (GEBRMAFSODC) is introduced. The main objective of the GEBRMAFSODC technique for performing energy-efficient data collection with lesser delay , data loss. Smart cities improve effectiveness of different applications including public transport services. By applying this method, the resource efficient optimal path and the population of artificial fishes (i.e. sensor nodes) is randomly initialized in the search space. For each node, fitness is measured depend on multivariate function namely energy, bandwidth, and distance. The Gradient Enhanced Broken-stick Regression is applied to fitness estimation for analyzing the resources and finding the optimal results. Efficient neighboring nodes are selected to transmit the collected data to sink node via best path. Sink node perform as a data collector with better resource sensor nodes through lesser delay. Simulation is conducted in NS2 simulator using Warrigal Dataset and the performance is analyzed by various parameters namely energy consumption, data collection delay, throughput, and data loss rate based on number of data. The observed result shows the superior performance of the proposed GEBRMAFSODC technique with a higher delivery ratio, throughput by 10%, 48% and lesser loss, delay, and energy consumption by 53%, 37%, and 27% as compared to other related methods respectively.

In this study, we examine the effects of channel and time-slot allocation algorithms on data loss events in multi-hop wireless networks. We investigate networks where all nodes have a single-radio antenna, the MAC layer protocol is synchronized OFDM, and each channel uses TDMA. In such networks, data collisions can be avoided by reserving data time-slots. Data loss can also occur when a receiving node changes its assigned channel, hence it does not completely receive the data which is on the link. We call this type of loss data corruption. Our simulations show that if there are several available time slots to reserve, a proper channel and time-slot selection algorithm is required to prevent data corruption. Consequently, it is better that a node changes channels as infrequently as possible and any necessary change should take place at an appropriate time. The same is true about switching between receiving and sending modes, i.e. switching modes also leads to data loss, so it should be avoided whenever possible. In this study we propose a new algorithm to allocate channels and time-slots so as to decrease data loss.

IoT systems use sensors to collect data from smart environments and manage resources through data analysis. An IoT system deals with many connected devices with different network and hardware constraints in a real-world scenario. An IoT system needs to handle low-latency data analysis, security threats, internal vulnerabilities, and network disconnections, which cause data loss and incorrect decisions. Trustworthiness (also known as dependability) provides various features for an IoT end-to-end data flow, such as resilience, security, availability, reliability, scalability, maintainability, heterogeneity, hardware resources management, fault management policies, and data quality. This paper presents a survey on trustworthiness and dependability in IoT systems and proposes the Trustworthiness for IoT Framework (TW-IoT) to provide trustworthiness at the data level for mist and fog-based IoT systems. The TW-IoT framework provides data trustworthiness to ensure a continuous and uninterrupted operation of IoT data flow. We also discuss challenges and trade-offs related to data trustworthiness in IoT.

Wireless sensor network (WSN) with mobile sink serves a lot of industrial and agricultural monitoring applications. The data collection with WSN has been popularly known in many applications such as smart environments, health monitoring, habitat monitoring, surveillance and tracking systems. The mobile sink moves to the corresponding cluster and gathers the data and moves to the next cluster head (CH) for the same. The data loss and waiting time of the data in the CH results from energy consumption and work repetition. This article provides a novel energy efficient data collection scheme. The data collection is done by the sink in a Polling based M/G/1 server model. The cluster member (CM) sends the data in N threshold model to the CH. The CH gathers the data from the CM and reports to sink once it arrives nearby. The energy efficient cluster head selection algorithm is evaluated with the proposed model and the waiting time is analyzed. The data arrival from the CM to the CH is considered as Poisson in nature. The sink mobility with respect to polling system is analyzed. The sink data collection is done through dynamic polling mechanism based on the CH arrival rate. The proposed algorithm outperforms the modified low energy adaptive clustering hierarchy and energy-aware multi-hop routing protocol (M-GEAR) protocols in terms of lifetime, waiting time and throughput. The proposed algorithm provides high resistance to energy hole and HOTSPOT problem.

In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the “non-sampled” parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60% less energy and can handle non-stationary data more effectively.

Reliable end-to-end delivery service is one of the most important issues for wireless sensor networks in large-scale deployments. In this paper, a reliable data transport protocol, called the Data Forwarding Protocol (DFP), is proposed to improve the end-to-end delivery rate with minimum transport overhead for recovering from data loss in large-scale wireless sensor environments consisting of low speed mobile sensor nodes. The key idea behind this protocol is the establishment of multi-split connection on an end-to-end route, through the Agent Host (AH), which plays the role of a virtual source or a sink node. In addition, DFP applies the local error control and the local flow control mechanisms to multi-split connections, according to network state. Extensive simulations are carried out via ns-2 simulator. The simulation results demonstrate that DFP not only provide up to 30% more reliable data delivery, but also reduces the number of retransmission generated by data loss, compared with the TCP-like end-to-end approach.

Smart environments interpret data from sensors to determine users' context. A relevant issue for their development is how to deal with the changes they experience at runtime. Components can be added, change or fail at runtime, modifying the system topology. These changes in turn modify the available information, providing new or redundant data or making impossible to calculate some pieces of the context. Opportunistic planning methods address these issues monitoring the context and suspending or resolving goals when the appropriate conditions are met. Using this approach, a smart room is able to wait and take advantage of changes in the context of the system to achieve goals that it was not able to accomplish before. This paper describes the development and testing of this context-aware behavior using FAERIE, a framework for context management intended to provide a general architecture. The sample application runs over UbikSim, which is a smart environment simulator for testing and validation.

Emerging concepts and paradigms such as Internet of Things and Cyber-Physical Systems are influencing the development of e-health systems. State of the art e-health systems are designed to seamlessly integrate geographically dispersed specific devices, sensors. Advances in Telemedicine systems are also facilitating the development of such systems. The authors discuss the development of a Redundant GSM and Satellite Data Transmission Device with application in tlelemedicine. The device is able to transmit data through a GSM modem and switch to a secondary GSM modem when the connection parameters are no longer adequate. Moreover, when GSM networks fail or are not available the device is still able to transmit data by switching to a Satellite Connection. Such a device allow for the redundant connection of heterogenous devices. The prototype of the communication device with applications in telemedicine is analyzed in the case study section.

This study discusses the external consensus problem for non-identical networked multi-agent systems with network data loss, considering both uniform and random consecutive data losses induced by long periods of transmission failure. A novel approach using a combination of prediction strategy and constant control gain based on gain error ratio (GER) is proposed for solving the problem. The main contribution of this study is a simple and practical way of selecting control gain using GER to help overcome prediction inaccuracies caused by the prediction process during a long period of consecutive data loss. The feasibility and performance of the proposed consensus protocol is demonstrated through simulation and practical experiment.

ABSTRACTCosmic rays are a known problem in astronomy, causing both loss of data and data inaccuracy. The problem becomes even more extreme when considering data from a high-radiation environment, such as in orbit around Earth or outside the Earth’s magnetic field altogether, unprotected, as will be the case for the James Webb Space Telescope (JWST). For JWST, all the instruments employ nondestructive readout schemes. The most common of these will be “up the ramp” sampling, where the detector is read out regularly during the ramp. We study three methods to correct for cosmic rays in these ramps: a two-point difference method, a deviation from the fit method, and a y-intercept method. We apply these methods to simulated nondestructive read ramps with single-sample groups and varying combinations of flux, number of samples, number of cosmic rays, cosmic-ray location in the exposure, and cosmic-ray strength. We show that the y-intercept method is the optimal detection method in the read-noise-dominated regime, while both the y-intercept method and the two-point difference method are best in the photon-noise-dominated regime, with the latter requiring fewer computations.

This article explores the impact of denormalization techniques on query performance in IoT data warehouses while maintaining acceptable data redundancy. It analyzes normalized and denormalized approaches in a smart home IoT environment using Azure Synapse. Empirical testing (10,000–5 million records) shows that strategic denormalization combined with columnar storage optimization improves performance by up to 94%. Evaluating four key optimization techniques (Join Reduction, Columnar Storage, Query Complexity Optimization, Temporal Scaling Optimization), we find that denormalization initially increases storage needs by 16% (120 GB vs. 103.5 GB), but columnar compression reduces the final storage size by 50.4% (17.1 GB vs. 34.5 GB). The study provides practical insights into balancing query performance and data redundancy in high-speed IoT environments.

Conservation and resource management in small tropical islands: Trade-offs between planning unit size, data redundancy and data loss

In a world of continuously expanding amounts of data, retrieving interesting information from enormous data sets becomes more complex every day. Solutions for precomputing views on these big data sets mostly follow either an offline approach, which is slow but can take into account the entire data set, or a streaming approach, which is fast but only relies on the latest data entries. A hybrid solution was introduced through the Lambda architecture concept. It combines both offline and streaming approaches by analyzing data in a fast speed layer first, and in a slower batch layer later. However, this introduces a new synchronization challenge: once the data is analyzed by the batch layer, the corresponding information needs to be removed in the speed layer without introducing redundancy or loss of data. In this paper we propose a new approach to implement the Lambda architecture concept independent of the technologies used for offline and stream computing. A universal solution is provided to manage the complex synchronization introduced by the Lambda architecture and techniques to provide fault tolerance. The proposed solution is evaluated by means of detailed experimental results.

Optical inspection for rail surfaces usually suffers from low defect contrast and measurement errors due to the significant variation in surface reflectivity or gloss and harsh environment. This paper regards the measurement error region such as overexposure/underexposure and data loss as a special cause for low contrast, namely, the contrast value is zero. To improve the defect contrast, this paper proposes a structured light (SL) illumination method and an unsupervised measurement-informed neural network (MeaNet). Quantitative relationships between the proposed SL parameters (e.g. gradient, period) and typical defect characteristics (e.g. crack width, crack orientation, rail surface reflectivity) are investigated. MeaNet additively fuses new information from input images and embeds defect a priori information, achieving two to three times of contrast improvement for cracks than state-of-the-art methods. Meanwhile, MeaNet generates a defect image showing only defect texture for an extremely high defect contrast. This method can be easily extended to other surface inspection applications.

The recognition of dairy cow behavior is essential for enhancing health management, reproductive efficiency, production performance, and animal welfare. This paper addresses the challenge of modality loss in multimodal dairy cow behavior recognition algorithms, which can be caused by sensor or video signal disturbances arising from interference, harsh environmental conditions, extreme weather, network fluctuations, and other complexities inherent in farm environments. This study introduces a modality mapping completion network that maps incomplete sensor and video data to improve multimodal dairy cow behavior recognition under conditions of modality loss. By mapping incomplete sensor or video data, the method applies a multimodal behavior recognition algorithm to identify five specific behaviors: drinking, feeding, lying, standing, and walking. The results indicate that, under various comprehensive missing coefficients (λ), the method achieves an average accuracy of 97.87 % ± 0.15 %, an average precision of 95.19 % ± 0.4 %, and an average F1 score of 94.685 % ± 0.375 %, with an overall accuracy of 94.67 % ± 0.37 %. This approach enhances the robustness and applicability of cow behavior recognition based on multimodal data in situations of modality loss, resolving practical issues in the development of digital twins for cow behavior and providing comprehensive support for the intelligent and precise management of farms. • Modality Mapping Completion Network addresses multimodal data loss challenges. • Achieves 97.87 % accuracy and high robustness under varying data loss conditions. • Uses Transformers for effective cross-modal fusion of image and sensor data. • Adapts to real-world challenges like interference, occlusion, and harsh environments.