Abstract
ABSTRACT Wireless Sensor Network (WSNs) can perform transmission within themselves and examination is performed based on their range of frequency. It is quite difficult to recharge devises under adverse conditions. The main limitations are area of coverage, network’s lifetime and aggregating and scheduling. If the lifetime of a network should be prolonged, then it can become a success along with reliability of the data transferred, conservation of sensor and scalability. Through many research works, this challenge can be overcome which are being proposed and the network’s lifespan improved which can preserve the sensor’s energy. By schemes of clustering, a low overhead is provided and the resources are efficiently allocated thus increasing the ultimate consumption of energy and reducing interfaces within the sensor nodes. Challenges such as node deployment and energy-aware clustering can be considered as issues of optimization with regards to WSNs, along with data collection. An optimal solution can be gotten through evolutionary and SI algorithm, pertaining to Non-deterministic Polynomial (NP)-complete along with a number of techniques. In this work, Krill Herd Algorithm based clustering is proposed.
Highlights
Wireless Sensor Network (WSN) is a self-establishing network with numerous sensors without proper infrastructure
The advances in the arena of WSNs have led to many novel protocols that are intended exclusively for SNs in which an area which needs utmost focus is energy awareness
Energy efficiency and load balancing are challenges which are quite significant. Managing such a high population of nodes effectively can be done through clustering mechanisms which help to reduce the nodes’ energy consumption
Summary
Wireless Sensor Network (WSN) is a self-establishing network with numerous sensors without proper infrastructure. SNs can be either wired or wireless, can be optical or some other mode of communication, but the most suited one is the short-range wireless low-power communication mode. Information collection and wireless communication along with multiple other functions and many SNs work together in forming robust SNs. The concurrent task of sensing, gathering ecological information and processing, monitoring and relaying data to the users can be accomplished using the consolidation of sensor technology, embedded computing, distributed information processing and wireless communication. WSNs comprise battery-powered devices that are energy constrained; when the battery drains, the sensor nodes are generally inaccessible post installation during which an alternative source of energy is difficult to incorporate. Expanding the network’s lifetime is an important issue in the WSN [1]
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