Cross‐layered energy optimization with MAC protocol based routing protocol in clustered wireless sensor network in internet of things applications

Author(s):  
Nader Ajmi ◽  
Amina Msolli ◽  
Abdelhamid Helali ◽  
Pascal Lorenz ◽  
Ridha Mghaieth
2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096435 ◽  
Author(s):  
Muhammad Ilyas ◽  
Zahid Ullah ◽  
Fakhri Alam Khan ◽  
Muhammad Hasanain Chaudary ◽  
Muhammad Sheraz Arshed Malik ◽  
...  

Internet of things grew swiftly and many services, software, sensors-embedded electronic devices and related protocols were developed and still in progress with full swing. Internet of things enabling physically existing things to see, hear, think and perform a notable task by allowing them to talk to each other and share useful information while making decision and caring-on/out their important tasks. Internet of things is greatly promoted by wireless sensor network as it becomes a perpetual layer for it. Wireless sensor network works as a base-stone for most of the Internet of things applications. There are severe general and specific threats and technical challenges to Internet of things–based sensor networks which must overcome to ensure adaptation and diffusion of it. Most of the limitations of wireless sensor networks are due to its resource constraint objects nature. The specified open research challenges in Internet of things–based sensor network are power consumption, network lifespan, network throughput, routing and network security. To overcome aforementioned problems, this work aimed to prolong network lifetime, improve throughput, decrease packet latency/packet loss and further improvise in encountering malicious nodes. To further tune the network lifetime in terms of energy, wireless harvesting energy is suggested in proposed three-layer cluster-based wireless sensor network routing protocol. The proposed mechanism is a three-tier clustering technique with implanted security mechanism to encounter malicious activities of sensor nodes and to slant them into blacklist. It is a centred-based clustering protocol, where selection of cluster head and grid head is carried out by sink node based on the value of its cost function. Moreover, hardware-based link quality estimators are used to check link effectiveness and to further improve routing efficiency. At the end, excessive experiments have been carried out to check efficacy of the proposed protocol. It outperforms most of its counterpart protocols such as fuzzy logic–based unequal clustering and ant colony optimization–based routing hybrid, Artificial Bee Colony-SD, enhanced three-layer hybrid clustering mechanism and energy aware multi-hop routing in terms of network lifetime, network throughput, average energy consumption and packet latency.


2019 ◽  
Vol 14 (4) ◽  
pp. 503-517 ◽  
Author(s):  
Wei Hu ◽  
Huanhao Li ◽  
Wenhui Yao ◽  
Yawei Hu

This paper attempts to solve the problems of uneven energy consumption and premature death of nodes in the traditional routing algorithm of rechargeable wireless sensor network in the ubiquitous power Internet of things. Under the application environment of the UPIoT, a multipath routing algorithm and an opportunistic routing algorithm were put forward to optimize the network energy and ensure the success of information transmission. Inspired by the electromagnetic propagation theory, the author constructed a charging model for a single node in the wireless sensor network (WSN). On this basis, the network energy optimization problem was transformed into the network lifecycle problem, considering the energy consumption of wireless sensor nodes. Meanwhile, the traffic of each link was computed through linear programming to guide the distribution of data traffic in the network. Finally, an energy optimization algorithm was proposed based on opportunistic routing, in a more realistic low power mode. The experimental results show that the two proposed algorithms achieved better energy efficiency, network lifecycle and network reliability than the shortest path routing (SPR) and the expected duty-cycled wakeups minimal routing (EDC). The research findings provide a reference for the data transmission of UPIoT nodes.


2017 ◽  
Vol 22 (S5) ◽  
pp. 11057-11068 ◽  
Author(s):  
Lei Wang ◽  
Junyan Qi ◽  
Wanwan Xie ◽  
Zhizhong Liu ◽  
Zongpu Jia

2014 ◽  
Vol 513-517 ◽  
pp. 1127-1130
Author(s):  
Ya Liu ◽  
Zhen Xu

The development and application of the Internet of things and the basic structure of wireless sensor network are briefly introduced,routing protocol in wireless sensor network is studied in detail. Since the wireless sensor network is a kind of energy-limited and resource-limited network, the routing protocol must guarantee the smaller routing expenses and the reducing energy consumption. Based on the analysis and research of existing wireless sensor network routing protocol, this paper emphasizes on the detailed analysis of the hierarchy routing protocol LEACH algorithm.The paper also introduces two improved algorithms according to the limit of LEACH protocol.


2021 ◽  
Author(s):  
Ramdas Vankdothu ◽  
Hameed Mohd Abdul

Abstract This paper provides an effective Wireless Sensor Network(WSN) routing solution for Internet of Things(IoT) applications cognizant of congestion, security, and interference. Because several sources try to deliver their packets to a destination simultaneously, which is a common case in IoT applications. The proposed congestion and interference aware safe routing protocol is claimed to work in networks with high traffic. The signal to interference ratio (SINR), congestion level, and survival factor is used in our suggested procedure to estimate the cluster head selection factor first. The adaptive fuzzy c-means clustering method clusters the network nodes based on the cluster head selection factor. After that, data packets are encrypted using Adaptive Quantum Logic-based packet coding. Finally, the Adaptive Krill Herd (AKH) optimization method identifies the least congested corridor, resulting in optimal data transmission routing. The exploratory findings show that the provided strategy outperforms previous methodologies in network performance, end-to-end delay, packet delivery ratio, and node remaining energy level.


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