Table 1 - uploaded by Saeed Salah
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... simulate message exchanges between nodes we used a fixed message size of 4000 bit, and finally, it is worth mentioning that we assumed the network to be free of collisions. Table 1 lists the network parameters used in the testbed scenarios. The four WSN routing protocols surveyed in this study are already built-in MATLAB, which we believe is a very good thing since it enables us to get reliable and consistent results. ...
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Citations
... The node which initiates the query can determine the node from which most useful information can be gained by consuming least energy [2]. Directed Diffusion (DD) is an example of query based protocol [7]. The queries are written in High level Languages. ...
... In this way the energy consumed can be decreased and reliability of the sensor network can be increased [6]. In [7] few examples of multipath routing like SPIN, SPEED are explained. In negotiation based protocols, decision is made to eliminate duplicate data before sending process. ...
... Thus it tries to reduce energy consumption by reducing the unnecessary data before processing. This protocol is an advantage over flooding techniques in which repeated data are transmitted from one node to the other [7]. DD, Sequential Assignment Routing (SAR), Sensor Protocol for Information Via Negotiation (SPIN) are few protocols of this kind. ...
Wireless sensor network (WSN) has gained popularity because of its potential applications in diverse fields. The main function of these networks is to collect information by sensing the environment and sending the data to appropriate location. Since the sensors are constrained by energy, while performing various functions the WSN has to make sure that minimum energy is consumed and life of the entire network is increased. Reducing the energy that is being consumed by sensor is one of the significant design challenges for WSN. A number of routing techniques are found in the literature that tries to efficiently utilize the finite energy of the sensor nodes. But when the size of the WSN grows, the volume of data to be gathered, processed and disseminated by the sensor nodes increases largely. Processing and transmitting such a large amount of data is impractical because of the limited energy of the sensors. Thus, there is a need for applying Machine Learning (ML) algorithms in WSNs. This literature survey focuses on the routing protocols used in WSN and implementation of machine learning techniques to optimize the performance of WSN.
... Since the sensors have the tiny size, therefore it has limited amount of energy, memory and processing capacity. Sometime sensors send the correlated data to the BS which enhances the power utilization and congestion within the network [1][2][3]. The power utilization can be minimized during the transfer of all correlated data to the BS, if data is collected locally and data redundant technique is applied before the actual transfer. ...
