In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.

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An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Utilization of smart applications in various domains is facilitated pervasively by sensor nodes (SN) that are connected in a wireless manner and a number of smart things. Hazards due to internal and external attacks exist along with the advantages of the smart things and its applications. Security measures are influenced by three main factors namely scalability, latency and network lifespan, without which mitigation of internal attacks is a challenge. The deployment of SN based Internet of things (IoT) is decentralized in nature. However, centralized solutions and security measures are provided by most researchers. A data driven trust mechanism based on blockchain is presented in this paper as a decentralized and energy efficient solution for detection of internal attacks in IoT powered SNs. In grey and black hole attack settings, the message overhead is improved using the proposed model when compared to the existing solutions. In both grey and black hole attacks, the time taken for detection of malicious nodes is also reduced considerably. The network lifetime is improved significantly due to the enhancement of these factors.

https://irojournals.com/tcsst/V3/I1/06.pdf

A Data Driven Trust Mechanism Based on Blockchain in IoT Sensor Networks for Detection and Mitigation of Attacks

Trust management-based and energy efficient hierarchical routing protocol in wireless sensor networks

Healthcare is one of the most promising domains for the application of Internet of Things- (IoT-) based technologies, where patients can use wearable or implanted medical sensors to measure medical parameters anywhere and anytime. The information collected by IoT devices can then be sent to the health care professionals, and physicians allow having a real-time access to patients' data. However, besides limited batteries lifetime and computational power, there is spatio-temporal correlation, where unnecessary transmission of these redundant data has a significant impact on reducing energy consumption and reducing battery lifetime. Thus, this paper aims to propose a routing protocol to enhance energy-efficiency, which in turn prolongs the sensor lifetime. The proposed work is based on Energy Efficient Routing Protocol using Dual Prediction Model (EERP-DPM) for Healthcare using IoT, where Dual-Prediction Mechanism is used to reduce data transmission between sensor nodes and medical server if predictions match the readings or if the data are considered critical if it goes beyond the upper/lower limits of defined thresholds. The proposed system was developed and tested using MATLAB software and a hardware platform called "MySignals HW V2." Both simulation and experimental results confirm that the proposed EERP-DPM protocol has been observed to be extremely successful compared to other existing routing protocols not only in terms of energy consumption and network lifetime but also in terms of guaranteeing reliability, throughput, and end-to-end delay.

/pdf/eerp-dpm-energy-efficient-routing-protocol-using-dual-41q1k8bmio.pdf

EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT.

Due to their working environments, limited resources and communication characteristics, wireless sensor networks face some challenges including energy optimization and security enhancement to extend the network lifetime and guarantee the network security. Therefore, an energy-aware and trust-based routing protocol for wireless sensor networks using adaptive genetic algorithm called TAGA is proposed to not only resist common routing attacks and special trust attacks, but also minimize the energy consumption caused by data transmission. To this end, TAGA constructs the nodes’ comprehensive trust values based on their direct trust values considering the volatilization and adaptive penalty factors, and indirect trust values with the filtering mechanisms. In addition, a novel threshold function is presented to select the optimal cluster heads, which considers the dynamic changes of the nodes’ comprehensive trust values and residual energy. Finally, a genetic algorithm with adaptive crossover probability and mutation probability is applied to find the optimal secure routing for the cluster heads. The simulation results show that TAGA can reduce the number of packets discarded by malicious nodes when facing common attacks and special trust attacks, and effectively improve the energy efficiency compared to the relative secure routing protocols EOSR and IASR. 

Energy-Aware and Trust-Based Secure Routing Protocol for Wireless Sensor Networks Using Adaptive Genetic Algorithm

As a key component of the information sensing and aggregating for big data, cloud computing, and Internet of Things (IoT), the information security in wireless sensor network (WSN) is critical. Due to constrained resources of sensor node, WSN is becoming a vulnerable target to many security attacks. Compared to external attacks, it is more difficult to defend against internal attacks. The former can be defended by using encryption and authentication schemes. However, this is invalid for the latter, which can obtain all keys of the network. The studies have proved that the trust management technology is one of effective approaches for detecting and defending against internal attacks. Hence, it is necessary to investigate and review the attack and defense with trust management. In this paper, the state-of-the-art trust management schemes are deeply investigated for WSN. Moreover, their advantages and disadvantages are symmetrically compared and analyzed in defending against internal attacks. The future directions of trust management are further provided. Finally, the conclusions and prospects are given.

/pdf/trust-based-attack-and-defense-in-wireless-sensor-networks-a-3tgx184dvv.pdf

Trust-Based Attack and Defense in Wireless Sensor Networks: A Survey

For hierarchical wireless sensor network (WSN), the clustered routing protocol can effectively deal with large-scale application requirements, thereby, how to efficiently elect the secure cluster heads becomes very critical. Unfortunately, many current studies only focus on improving security while neglecting energy efficiency and transmission performance. In this paper, a lightweight trust management scheme (LTMS) is proposed based on binomial distribution for defending against the internal attacks. Simultaneously, distance domain, energy domain, security domain and environment domain are considered and introduced to propose a multidimensional secure clustered routing (MSCR) scheme by using dynamic dimension weight in hierarchical WSNs. The simulation results show that LTMS can effectively prevent a malicious node from being elected as a cluster head, and MSCR can achieve a balance between security, transmission performance and energy efficiency under the requirements of environmental applications.

/pdf/mscr-multidimensional-secure-clustered-routing-scheme-in-5efmkvhn0e.pdf

Yepeng Ni

Papers

Trust-Based Attack and Defense in Wireless Sensor Networks: A Survey

MSCR: multidimensional secure clustered routing scheme in hierarchical wireless sensor networks