The Merits of a Decentralized Pollution-Monitoring System Based on Distributed Ledger Technology

Pollution-monitoring systems (PMSs) are used worldwide to sense environmental changes, such as air quality conditions or temperature increases, and to monitor compliance with regulations. However, organizations manage the environmental data collected by such PMSs in a centralized manner, which is why recorded environmental data are vulnerable to manipulation. Moreover, the analysis of pollution data often lacks transparency to outsiders, which may lead to wrong decisions regarding environmental regulations. To address these challenges, we propose a software design for PMSs based on distributed ledger technology (DLT) and the long-range (LoRa) protocol for flexible, transparent, and energy-efficient environment monitoring and data management. To design the PMS, we conducted a comprehensive requirements analysis for PMSs. We benchmarked different consensus mechanisms (e.g., BFT-SMaRt and Raft) and digital signature schemes (e.g., ECDSA and EdDSA) to adequately design the PMS and fulfill the identified requirements

Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model

Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime.This work is supported by the National Science Foundation of China under Grand No. 61370210 and the Development Foundation of Educational Committee of Fujian Province under Grand No. 2012JA12027.Cheng, H.; Su, Z.; Lloret, J.; Chen, G. (2014). Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model. Sensors. 14(11):20940-20962. https://doi.org/10.3390/s141120940S2094020962141

Node Selection Algorithms with Data Accuracy Guarantee in Service-Oriented Wireless Sensor Networks

The service-oriented architecture is considered as a new emerging trend for the future of wireless sensor networks in which different types of sensors can be deployed in the same area to support various service requirements. The accuracy of the sensed data is one of the key criterions because it is generally a noisy version of the physical phenomenon. In this paper, we study the node selection problem with data accuracy guarantee in service-oriented wireless sensor networks. We exploit the spatial correlation between the service data and aim at selecting minimum number of nodes to provide services with data accuracy guaranteed. Firstly, we have formulated this problem into an integer nonlinear programming problem to illustrate its NP-hard property. Secondarily, we have proposed two heuristic algorithms, namely, Separate Selection Algorithm (SSA) and Combined Selection Algorithm (CSA). The SSA is designed to select nodes for each service in a separate way, and the CSA is designed to select nodes according to their contribution increment. Finally, we compare the performance of the proposed algorithms with extended simulations. The results show that CSA has better performance compared with SSA

Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks

The sensing data of nodes is generally correlated in dense wireless sensor networks, and the active node selection problem aims at selecting a minimum number of nodes to provide required data services within error threshold so as to efficiently extend the network lifetime. In this paper, we firstly propose a new Cover Sets Balance (CSB) algorithm to choose a set of active nodes with the partially ordered tuple (data coverage range, residual energy). Then, we introduce a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. Finally, we propose a High Residual Energy First (HREF) node selection algorithm to further reduce the number of active nodes. Extensive experiments demonstrate that HREF significantly reduces the number of active nodes, and CSB and HREF effectively increase the lifetime of wireless sensor networks compared with related works.This work is supported by the National Science Foundation of China under Grand nos. 61370210 and 61103175, Fujian Provincial Natural Science Foundation of China under Grant nos. 2011J01345, 2013J01232, and 2013J01229, and the Development Foundation of Educational Committee of Fujian Province under Grand no. 2012JA12027. It has also been partially supported by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," Project TEC2011-27516, and by the Polytechnic University of Valencia, though the PAID-15-11 multidisciplinary Projects.Cheng, H.; Su, Z.; Zhang, D.; Lloret, J.; Yu, Z. (2014). Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks. 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