Wireless sensor network (WSN) comprises of huge amount of sensing nodes being used for collecting the data in different situations. WSN finds applications mostly to gather information from remote places such as in environment monitoring, military, in transportation security and so on. The main problem in WSN is the availability of limited energy resources. For enhancing energy efficiency with the lifespan of sensor nodes, in this research, an energy effective routing protocol, low energy adaptive clustering hierarchy (LEACH) along with optimization algorithm genetic algorithm (GA) has been presented. LEACH is a hierarchical protocol which converts the sensor nodes into cluster heads (CH), and CH gathers and compress the data and send it to the target node. Genetic algorithm helps to find the optimal route by using its fitness function. After simulating the code in MATLAB, the energy consumption rate up to 17.39% has been reduced when GA is used. At last, comparison between proposed work and existing work is performed to determine the efficiency of the proposed work.

Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks

Energy Efficient Resource Allocation Algorithm for Agriculture IoT

Considering the important applications of wireless sensor networks in target monitoring, it has received attention in the civil and military fields. For the problem that the working nodes die early, the network energy consumption is large, and the node energy distribution is uneven, especially the impact of the monitored target on the network performance, this study is based on the traditional low-energy adaptive clustering hierarchy (LEACH) and target location constraint, an improved LEACH target location constraint (LEACH-TLC)
 algorithm is proposed. First, under the premise of monitoring accuracy, this study constructs a network monitoring performance parameter model. Second, the wake-up/dormant threshold of the network node is obtained based on the network performance parameter model, and the network node dormant strategy is constructed utilising the threshold, and then the working node set is constructed. Finally, based on the proposed network energy uniform mechanism, combined with the distance factor between the node and target/base station, a new cluster head selection rule is proposed. The simulation results show that the LEACH-TLC algorithm can decrease the number of working nodes, reduce network energy consumption, uniform node energy consumption distribution, and prolong network lifetime.

LEACH-TLC: a strategy of reducing and uniform energy consumption based on target location constraint

Multiparameter optimization system with DCNN in precision agriculture for advanced irrigation planning and scheduling based on soil moisture estimation

In the recent days, the Internet of Things (IoT) has assumed a significant role in automized farming applications. The farmers can receive frequent information corresponding to the soil moisture, water used, humidity, temperature, moisture, etc., by utilizing actuators and sensor nodes. Various clustering techniques used in wireless sensor network (WSN) applications have achieved energy‐efficient results. However, IoT‐enabled smart farming (SF) applications require scalable networks and long‐distance communication. The existing clustering techniques cannot provide SF solutions, and work related to communication overhead, end‐to‐end delay, latency etc., has not been taken up exhaustively. Therefore, in this article, a cross‐layer‐based hybrid particle Swarm wild horse optimizer (PSWHO) and stable routing technique (CL‐HPWSR) is designed to minimize energy consumption, communication delay, latency etc. in SF applications. Initially, the network is designed to be Bi‐concentric Hexagons. The cross‐layer‐based optimal cluster head (CH) selection mechanism is designed using the hybrid optimization algorithm‐named PSWHO to mitigate excess energy consumption problems in WSN. While clustering, cross‐layer parameters from different layers such as physical, medium access control, and network are used to compute the probability of each sensor node. Moreover, the deep learning‐based routing algorithm extended deep golden eagle neural network (EDGENN) is introduced to explore the optimal route for transmitting the data. The performance of CL‐HPWSR is implemented in Matlab by focusing energy‐efficiency, computational‐efficiency and QoS‐efficiency factors. Finally, in comparison with state‐of‐the‐art‐based SF approaches such as CL‐IoT, FEEC‐IIR, BiHCLR, PSO‐ECHS, and PSO‐C, the CL‐HPWSR has performed better in terms of energy‐efficiency, QoS‐efficiency and computational‐efficiency.

CL‐HPWSR: Cross‐layer‐based energy efficient cluster head selection using hybrid particle swarm wild horse optimizer and stable routing in IoT‐enabled smart farming applications

Crop production is significantly decreasing due to inefficient agricultural practices, limited irrigation facilities and lack of technology. Economic conditions of developing countries force farmers to use traditional practices rather than adopting latest technologies which are capable of producing better yield. Practices like Precision Agriculture are being incorporated all over the world to help farmers to get better crop yield. Precision Agriculture is the concept of real-time observation of soil parameters like temperature, water content, moisture etc. In present work, Wireless Sensor Network (WSN) has been implemented to sense and store data of agricultural field. To increase the acceptance of this technology in agriculture, cost effective and energy efficient system is required. Modified Zonal Stable Election Protocol (M-ZSEP) which follows hybrid routing for heterogeneous networks has been realized for agricultural field. WSN collects data through real time monitoring and then transmit it to the Base Station (BS). Data received is then displayed at base station to achieve monitoring of various soil parameters for efficient use of resources. Result indicate that M-ZSEP outperforms traditional protocols in terms of throughput and stability period which in turn will increase the lifetime of nodes and further make proposed network energy efficient.

Energy Efficient WSN for Precision Agriculture – Using Modified Zonal Stable Election Protocol

In wireless sensor networks (WSN), energy efficiency has been the main concern. Analysis of threshold sensitive modified LEACH protocol has been done in this paper. For the identical amount of initial energy and election probability the number of rounds, which a node lives in threshold, sensitive modified LEACH protocol is to a great extent higher than the traditional LEACH protocol suggested by critical comparative analysis and simulation results. Modified LEACH protocol mainly involves the modified cluster head scheme as well as the dual transmitting power level. The threshold sensitive modified LEACH protocol outruns the traditional LEACH protocol in terms of energy efficiency as shown by the results obtained.

Sindhu Hak

Papers

Energy Efficient WSN for Precision Agriculture – Using Modified Zonal Stable Election Protocol

Threshold Sensitive Modified Leach Protocol for Energy Saving in Wireless Sensor Network