Higher Data Rate Transmission for Underwater Wireless Sensor Communications Using Industrial, Scientific, and Medical (ISM) Bands

Introduction

It is known fact that for underwater communication system, lower frequency bands are used which also effects the data rate transmission at lower rate which are due to physical phenomena such as reflections, refraction and energy dispersion. These above said problems can be mitigated by installing wireless sensors network which are mandatory placed closed to each other and hence for accurate measurements, higher communication bandwidth is required. By using real scenario for measurement at higher data rate, 2.40GHz ISM band is used efficiently for underwater communication system with efficient use of energy. Wireless sensor network has drawn lots of attention for variety of applications. This wireless technology system suffers demerit related to consumption of energy when the number of nodes gets increases. Alternatively, batteries can be powered to run this existing wireless sensor network but using them for underwater wireless communication is also a bigger problem as the issue related to charge them arises. Thus an alternate solution for saving of power becomes necessary (Sendra et al., 2011). A group of cluster cells can be used as underwater local area network for underwater sensor network. By having several nodes in the cluster, data rate can be increased with multiple hops. It is also known that the selection of aggregators, intra-routing (routing from sensor to aggregator) and inter routing (routing from aggregator to sink) are the three main steps involved in data aggregation. By using Fuzzy Logic technique, clustering technique and aggregation can be simplified and optimize the parameters such as residual energy, distance to sink, Node density and link quality (Goyal et al., 2014). One of the major problems faced in underwater wireless communication is that the wireless sensor network gets affected leading to failure of cluster head or cluster member. Fault detection and recovery technique finds the solution for above said problems (Goyal et al., 2018). Electrical property of the wireless system including permeability of the data of liquid water plays vital role in underwater communication system for frequencies upto 1THz which is characterized by double Debye model (Liebe et al., 1991). Underwater communication system requires large amount of energy and also long data transmission of packet thus by effecting cluster head, cluster size and routine scheme discussed earlier. By application of Fuzzy logic in selection of cluster head and cluster size, improvement in the performance in the underwater sensor networks is observed (Goyal et al., 2016). Industrial, Scientific and Radio Band (ISM) working at microwave frequency of 2.40GHz eliminates low frequency demerits for underwater communication such as reflections, refractions, energy dispersion etc. For more accurate measurement in underwater, it becomes necessary that wireless sensors needs t be placed close to each other (Lloret et al., 2012). By using sleep-wake up algorithm, aggregation of data is improved at the cluster head and thus by scheduling of data transmission, power required for underwater communication can be reduced (Goyal et al., 2017a). Cluster based underwater sensor communication networks can be also used for congestion control and load balancing technique in underwater-wireless-sensor-network for cluster. Also the network performance is enhanced by controlling the congestion and balancing the traffic (Goyal et al., 2016). At lower frequencies, communication link can be established for very longer distance which also prevents power losses that are generated for higher frequencies. By keeping frequency as low as 3 KHz can achieve communication of about 40 meters between nodes. Due to the advantage of lower error probability, BPSK modulation system is best suited for underwater wireless communication (Sendra et al., 2011). Path loss of the signal traveling from one end to other is also a major concern in underwater communication system and hence restriction in use of RF signals is limited (Chaitanya et al., 2011). Also, the nodes which are created for sensor networks are combined and monitored for underwater communication for a defined smaller area (Goyal et al., 2017b). Also, the dielectric constant of the water is much higher which effects the propagation parameters of electromagnetic waves used for underwater communication. These few parameters are skin depth, total path loss and frequency function which decides the distance of wireless communication between transmitter and receiver (Chakraborty et al., 2009). Potentially, three different technologies including radio, acoustic and optical form the basis of underwater communication system. It was noted that for small coastal erosions Electromagnetic based communication system for underwater communication provided different advantages when compared with the other two systems (Che et al., 2010). It is also known that aggregate data base is divided into cluster or non-cluster category. Underwater clustering scheme and round based clustering scheme are used generally for underwater communication (Goyal et al., 2019c). There are number of underwater communication system based on sensor network such as collection of data related to graphical structure of ocean, disaster/ prevention management etc. Communication related to underwater involves collection of data from the nodes and then communicate with the sink/load (Goyal et al., 2019a). Underwater communication system suffers drawbacks such as large delay, node mobility and limitation in link capacity (Kumar et al., 2014). An adaptive error control mechanism can overcome demerits related to underwater sensor network and also different architecture for 2-Dimension or 3-Dimension sensor network is proposed (Goyal et al., 2018b: Akyildiz et al., 2004). Also, a PN-sequence (pseudo noise) based underwater communication can be used where a modulated by using binary-shift-keying-technology (Nguyen et al., 2017). Also reactive protocols are generally used for underwater communication network (Garcia et al., 2012: Yadav et al., 2019). By calculating transmission propagation loss of EM penetrating from air to water generalizes the evidence for applications of RF waves in applications for different types of water system (Jiang et al., 2011: Liu et al., 2008). There are various technologies has been proposed for underwater communication system such as CAN (Cognitive Acoustic Networks), SDN (Software Defined Networks), NFV (Network Function Virtualization), Cloud-Computing, Fog-Computing and internet of underwater-communication things. (Huma et al., 2019). It is well known fact that radio waves as well as optical waves suffers huge attenuation for communication in underwater communication networks. Based on the assumption that unknown nodes are moving and with actual speed of light, proposed algorithm reveals that good performance is achieved which is non-sensitive to variations in velocity of sound and movement of node underwater (Shaochen et al., 2019). Considering applications in commercial as well as military fields, positioning technology for underwater communication network is very sensitive and important. Challenges such as fluidity, sparse installation with limitation of energy are to be taken care (En et, al., 2019).