Wireless Sensor Network Architecture Based on Mobile Edge Computing

Abstract

Wireless sensor networks (WSN) are infrastructure-less and fully distributed systems of self-configurable and self-organized nodes that wish to share the IoT devices’ data over the air. WSN consists of heterogeneous/homogeneous sensors dispersed in a particular area to monitor phenomena, such as volcanoes, pressure, sound, temperature, and pollution, then transfer their data to a central location for analysis and decision-making. The deployment of WSNs is often used in difficult places, such as disaster areas, battlefields, conflict zones, and volcanic zones, which require efficient data collection. However, WSNs face many challenges, such as latency, multitasks, and data quality. This paper proposes an efficient solution for multiple data collection tasks exploiting mobile edge computing (MEC) technique-enabled WSN in war field and disaster areas. Firstly, we integrate WSN and MEC to produce a novel data collection framework (WSN). Secondly, we modelled the data collection process, including multiple sensors and tasks. Thirdly, determine the best way to deploy sensors in the targeted area (e.g., military areas, isolated areas, and areas infested with terrorists) to cover the whole destination area, based on set values for data quality. Finally, find an appropriate technique for routing by choosing the routing protocol to improve their capabilities performance and decrease the energy consumption to increase the life of the WSN. Furthermore, the security of WSN is one of the key parameters, especially in the routing protocol, since it is critical to ensure the security of transmitted data and how it is transmitted securely to mitigate the attacks. The framework which works as a simulation tool shows a good result and has been proven the efficient interaction between the MEC and WSN. We compare the results with traditional methods, which proved to outperform them.

Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs

“…The communication subsystem is also the main source of energy consumption because the distance between the source and destination nodes determines how much energy is used to convey a message [17,18]. Thus, multi-hop communication can reduce the distance required in transmission, which comes at the expense of increasing the delay compared to single-hop communication [17,19]. Furthermore, the communication between sensor nodes is accomplished according to the IEEE 802.15.4 standard, keeping in mind that the communication range depends on the sensor node's energy.…”

Performance Analysis of Sink Mobility Models for Wireless Sensor Networks: A Comparative Study

“…Conversely, the energy consumption of SNs is uneven when multi-hop routing is used instead of S-H routing, especially for SNs close to the base station, since most of the traffic generated by remote SNs would pass through these nodes. Therefore, most of these SNs' energy will be consumed to relay messages from remote nodes [6] [8].…”

Using Minimum Connected Dominating Set for Mobile sink path planning in Wireless Sensor Networks

“…In contrast, adopting multi-hop rather than single-hop routing comes at the cost of increasing the delay and causing unbalanced energy consumption between sensor nodes, particularly for sensor nodes located near the base station because most of the traffic generated by distant sensor nodes will pass through these nodes. Therefore, these sensor nodes will spend most of their energy passing messages generated by other sensor nodes dispersed far from the base station [5], [7].…”