Recently, significant growth in the applications of the wireless sensor networks (WSNs) operating in unlicensed spectrum bands has been made. In the meantime, existing unlicensed bands are becoming congested and overcrowded. On the other hand, Cognitive Radio (CR) technology has been proposed as the key technology for future wireless communication that exploits dynamic spectrum access strategies. Since this offers more flexible way to utilize the wireless spectrum than traditional radio technology based on a fixed spectrum access policy, it is thought that the key feature of cognitive radio i.e. dynamic spectrum access can be applied in WSNs to access the underutilized spectrum bands to transmit their readings in an opportunistic manner to the next hops and finally to the sink. In this paper, we discuss the basic responsibilities of MAC layer and its functionalities in cognitive radio sensor networks (CRSN). We mainly focus on investigating the MAC layer issues while realizing the cognitive capability into wireless sensor networks.
In Wireless Sensor Networks, network lifetime optimization has challenging and significant issue. Subsequently, most of the existing works delineate several factors to improve the network lifetime: by decreasing the amount of the consumption of energy, reducing latency, load balancing, clustering, efficient data aggregating and by minimizing the data transmission delays. This paper provides a review of recent techniques and presents a Machine Learning-based Optimized Hierarchical Routing Protocols for WSN Lifetime. Research has been done, and reviews have been studied to explore the energy management schemes using optimized routing approach and Machine Learning Adaptability for WSN’s. Further, recommend future directions related to the Optimized Clustering Approaches to enhance wsn lifetime.
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