An energy efficient coverage guaranteed greedy algorithm for wireless sensor networks lifetime enhancement

One of the most significant difficulties in Wireless Sensor Networks (WSNs) is energy efficiency, as they rely on minuscule batteries that cannot be replaced or recharged. In battery-operated networks, energy must be used efficiently. Network lifetime is an important metric for battery-powered networks. There are several approaches to improve network lifetime, such as data aggregation, clustering, topology, scheduling, rate allocation, routing, and mobile relay. Therefore, in this paper, the authors present a method that aims to improve the lifetime of WSN nodes using a greedy algorithm. The proposed Greedy Algorithm method is used to extend the network lifetime by dividing the sensors into a number of disjoint groups while satisfying the coverage requirements. The proposed Greedy algorithm has improved the network lifetime compared to heuristic algorithms. The method was able to generate a larger number of disjoint groups

IMPROVING NETWORK LIFETIME BY MINIMIZING ENERGY HOLE PROBLEM IN WSN FOR THE APPLICATION OF IoT

The world today is at the Internet of Things (IoT) inflection point with more number of products adding to its intelligence system through a wide range of connectivity. Wireless sensor Networks (WSN) have been very useful in IoT application for gathering and processing of data to the end user. However, limited battery power and network lifetime are few of the major challenges in the designing process of any sensor network. One of those  is the Energy Hole Problem (EHP) that arises when the nodes nearer to the sink or base station die out early due to excess load as compared to other nodes that are far away. This breaks the connection of the network from the sink which results in shortening the lifetime of the network. In this paper, a trade-off is maintained between network lifetime and power requirement by implementing a sleep-awake mechanism.With the help of MATLAB simulations, it is found that after applying the mechanism, the network lifetime was extended to almost 300 and 700 rounds for TEEN and LEACH protocol respectively. The results will be beneficial for the design process in WSN for IoT application

An Optimal Task Scheduling Algorithm in Wireless Sensor Networks

Sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. To minimize the execution time (makespan) of a given task, an optimal task scheduling algorithm (OTSA-WSN) in a clustered wireless sensor network is proposed based on divisible load theory. The algorithm consists of two phases: intra-cluster task scheduling and inter-cluster task scheduling. Intra-cluster task scheduling deals with allocating different fractions of sensing tasks among sensor nodes in each cluster; inter-cluster task scheduling involves the assignment of sensing tasks among all clusters in multiple rounds to improve overlap of communication with computation. OTSA-WSN builds from eliminating transmission collisions and idle gaps between two successive data transmissions. By removing performance degradation caused by communication interference and idle, the reduced finish time and improved network resource utilization can be achieved. With the proposed algorithm, the optimal number of rounds and the most reasonable load allocation ratio on each node could be derived. Finally, simulation results are presented to demonstrate the impacts of different network parameters such as the number of clusters, computation/communication latency, and measurement/communication speed, on the number of rounds, makespan and energy consumption