ArticleSensors (Basel, Switzerland)2018
A Trust-Based Secure Routing Scheme Using the Traceback Approach for Energy-Harvesting Wireless Sensor Networks.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Enhanced lion swarm optimization and elliptic curve cryptography scheme for secure cluster head selection and malware detection in IoT-WSN.Scientific reports · 2024Article
- Micro energy harvesting for IoT platform: Review analysis toward future research opportunities.Heliyon · 2024Review
- Joint Optimization of Energy Consumption and Data Transmission in Smart Body Area Networks.Sensors (Basel, Switzerland) · 2022Article
- An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks.Sensors (Basel, Switzerland) · 2018Article
- Differentiated Data Aggregation Routing Scheme for Energy Conserving and Delay Sensitive Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2018Article
- Caching Joint Shortcut Routing to Improve Quality of Service for Information-Centric Networking.Sensors (Basel, Switzerland) · 2018Article
- A General Cross-Layer Cloud Scheduling Framework for Multiple IoT Computer Tasks.Sensors (Basel, Switzerland) · 2018Article
- RCSS: A Real-Time On-Demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks.Sensors (Basel, Switzerland) · 2018Article
- An Effective Delay Reduction Approach through a Portion of Nodes with a Larger Duty Cycle for Industrial WSNs.Sensors (Basel, Switzerland) · 2018Article
- An Adaption Broadcast Radius-Based Code Dissemination Scheme for Low Energy Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2018Article
- A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs.Sensors (Basel, Switzerland) · 2018Article
- LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.Sensors (Basel, Switzerland) · 2018Article
- Adaptive Aggregation Routing to Reduce Delay for Multi-Layer Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2018Article
- Edge-Based Efficient Search over Encrypted Data Mobile Cloud Storage.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
6 authors.
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Abstract
The Internet of things (IoT) is composed of billions of sensing devices that are subject to threats stemming from increasing reliance on communications technologies. A Trust-Based Secure Routing (TBSR) scheme using the traceback approach is proposed to improve the security of data routing and maximize the use of available energy in Energy-Harvesting Wireless Sensor Networks (EHWSNs). The main contributions of a TBSR are (a) the source nodes send data and notification to sinks through disjoint paths, separately; in such a mechanism, the data and notification can be verified independently to ensure their security. (b) Furthermore, the data and notification adopt a dynamic probability of marking and logging approach during the routing. Therefore, when attacked, the network will adopt the traceback approach to locate and clear malicious nodes to ensure security. The probability of marking is determined based on the level of battery remaining; when nodes harvest more energy, the probability of marking is higher, which can improve network security. Because if the probability of marking is higher, the number of marked nodes on the data packet routing path will be more, and the sink will be more likely to trace back the data packet routing path and find malicious nodes according to this notification. When data packets are routed again, they tend to bypass these malicious nodes, which make the success rate of routing higher and lead to improved network security. When the battery level is low, the probability of marking will be decreased, which is able to save energy. For logging, when the battery level is high, the network adopts a larger probability of marking and smaller probability of logging to transmit notification to the sink, which can reserve enough storage space to meet the storage demand for the period of the battery on low level; when the battery level is low, increasing the probability of logging can reduce energy consumption. After the level of battery remaining is high enough, nodes then send the notification which was logged before to the sink. Compared with past solutions, our results indicate that the performance of the TBSR scheme has been improved comprehensively; it can effectively increase the quantity of notification received by the sink by 20%, increase energy efficiency by 11%, reduce the maximum storage capacity needed by nodes by 33.3% and improve the success rate of routing by approximately 16.30%.
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