DEEP LEARNING APPROACH FOR EFFICIENT ENERGY CONSUMPTION AND HIGH THROUGHPUT IN MOBILE WIRELESS SENSOR NETWORKS
Creators: name: Alsalmi, Nada
Publisher: name: Lancaster University
Year: 2024

description: Features such as scalability, smaller size, simplicity, low-cost
 operation, self organization abilities, and easy and fast deployment are
 the main parameters of a Wireless Sensor Network (WSN). The research
 demand is growing on WSNs, and therefore, areas under agriculture,
 industry, healthcare, manufacturing, security, surveillance, transport,
 air quality, water quality, etc., have started to possess the attributes
 of WSNs.
 
 
 The primary goal of SNs is to collect the data from the area of interest
 and
 
 communicate it to the sink or base station (BS) for further processing via
 single
 
 or multi-hop transmission. Sometimes, the BS acts as a gateway to the
 Internet
 
 of Things (IoT), where the IoT can communicate the data to the Cloud using
 
 the Internet. The battery-equipped SNs consume more energy for heavy data
 
 transmission. Transmission of high-quality data in SN makes the
 battery-equipped micro-sensors consume much energy.
 
 
 Mobile Wireless Sensor Network (MWSN) represents a fast-evolving
 technology, and its use in many things is not limited. While
 fixed-infrastructure networks
 
 constrain sensor nodes to one specific location, MWSNs allow the partial
 nodes
 
 or all nodes to move wherever they want and communicate between
 themselves, making the whole system more flexible. Furthermore, MWSNs can
 be compared with respect to GPS, Bluetooth Low Energy (BLE), and existing
 wireless sensor networks in aspects of extended network lifespan, energy
 saving, multiband functionality, and high targeting. Nevertheless,
 pathfinding in MWSNs is very challenging since the sensor nodes are
 mobile, low-cost devices that are time constrained, allowing limited
 resources to be used. On the mobile network, this unique frequency scheme
 creates extra difficulty in routing. In most monitoring applications, only
 partial nodes need to be moved in the network. Such nodes are called
 mobile agent sink nodes or sensor nodes. In the present work, the movement
 of only a few nodes is considered in MWSN.
 
 
 Energy consumption and network connectivity are two major issues in MWSNs.
 
 Several studies have been conducted to develop and propose suitable
 solutions
 
 for these problems. Many researchers are working to develop the best
 solutions
 
 due to the severe problems with energy consumption and network
 connectivity
 
 in mobile wireless sensor networks. To investigate network connectivity,
 this
 
 study introduces a new efficient technique that considers parameters like
 network stability, detection area, low energy consumption, etc. This
 approach guarantees network connectivity, communication sustainability,
 and the highest level of energy consumption optimization.
 
 
 This research investigates network connectivity issue and proposes two
 routing
 
 algorithms, namely Self-Organizing Maps based-Optimized Link State Routing
 
 (SOM-OSLR) and Deep Reinforcement Learning based-Optimized Link State
 
 Routing (DRL-OLSR) for MWSNs. Both algorithms undertake the relationship
 
 between sensor node deployment, communication radius, and detection area
 and suggest a new way to maintain communication while optimizing energy
 usage. I have evaluated both algorithms through simulations by considering
 various performance metrics such as connection probability, end-to-end
 delay, overhead, network throughput, and energy consumption.
 
 
 The network is analyzed for proposed routing and aggregation methods to
 
 analyze the performance. The simulation analysis is discussed under three
 
 scenarios. The first scenario undertakes ’no optimization,’ the second
 considers
 
 SOM-OLSR, and the third undertakes DRL-OLSR. The simulation results
 indicate
 
 that the SOM-OLSR performs better compared to the case with ’no routing’
 
 optimization. Comparing DRL-OLSR and SOM-OLSR indicates that the former
 
 outperforms the latter in terms of low latency and high network lifetime.
 
 Specifically, the DRL-OLSR achieves a 47% higher throughput and 67% lower
 
 energy consumption compared to the SOM-OLSR. In addition, when compared to
 the ’No optimization’ condition, the DRL-OLSR achieves a notable 69.7%
 higher throughput and almost 89% lower energy consumption. These findings
 highlight the effectiveness of the DRL-OLSR approach in optimizing network
 performance and energy efficiency in wireless sensor networks. Similarly,
 data aggregation consistently reduces energy consumption across all
 scenarios, with up to 50% lower as compared to without data aggregation.