Energy Positioning Factors For Wireless Sensor Network

The Hierarchical Power Aware Routing (HPAR) is a power aware routing protocol that divides the network into a group of sensors called zones [12]. In this, the cluster zones are formatted, then the decision is made that how message is routed so the battery life can be maximized. This protocol provides an approximation algorithm called max-min ZPmin algorithm. In this algorithm by applying Dijkshtra algorithm the path who consumes less power is found. Then the second path is found that maximizes the minimal residual power. The advantage is that it provides both transmission power and maximizes battery

A WSN is a type of wireless networks that consists of collection sensor nodes which are tiny devices. Each sensor node of the network has different processing capability. It may contain multiple types of memory (program, data and flash memories), have a RF transceiver, have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion [13, 14]. Optimum need of each sensor node is to maximize its own utility function. Also the whole network requires resource assignments balance to perform in a useful and efficient way. This chapter presents a brief survey on WSNs showing its types, characterizing features, protocols and applications.

Abstract. In this paper we opt for an experimental approach and used various types of real –world different environment conditions determine the actual power consumption of wireless sensor nodes with different set of batteries for each different environment. We took reading in different environment such as Savage Arena, Auditorium, Lab and parking lot. Some of our observations are close to the previously published papers. We got these results from widely used Zolertia Z1 motes with particular grid patterns. We have compared these results with simulated results in Contiki Os

Abstract - Wireless Sensor Networks (WSN) comprises of several tiny, low-cost, resource constrained sensor nodes. These nodes are placed in harsh environments and generally are used for air pollution monitoring, water quality monitoring, industrial monitoring, health monitoring and more. Routing is difficult in such surroundings primarily due to the unique constraints the wireless sensor networks suffer from. Wireless sensor network is highly dynamic, making existing routing protocols ineffective. This paper concentrates on energy efficiency of the protocols. Both the protocol presented are hierarchical and cluster based. Both have sensor nodes and a base station (BS). The BS selects the Cluster Heads (CH) among themselves. CH is the elected sensor node which passes on the sensor data collected by sensor nodes of its cluster to either BS or other CH. All candidate nodes for becoming CH are listed, based on the various factors like relative distance of the candidate node from the Base Station, outstanding energy level, possible number of neighboring sensor nodes the

Wireless sensor nodes[2] have limited resources such as limited memory and limited power. There is unreliable communication in wireless sensor network that leads to threat to sensor security. The security of network depend on communication which relies on protocol.

Arbi, I. B., Derbel, F., & Strakosch, F. (2017, May). Forecasting methods to reduce energy consumption in WSN. In Instrumentation and Measurement Technology Conference (I2MTC), 2017 IEEE International (pp. 1-6). IEEE.

The main goal of communication protocols in WSNs is to maximize energy efficiency in order to

In this paper, I will mainly focus on analyzing and evaluating the current Wireless Sensor Networks (WSN). This paper starts with an overview on fundamentals of WSNs, by providing necessary background to understand

Wireless Sensor Network (WSN) consists of a large number of low cost sensor nodes depolyed in parking area. These sensor nodes form a multi-hop ad-hoc network by wireless commnunication.

Wireless sensor networks typically consist of a large number of sensor nodes with limited onboard battery resources which are difficult to recharge or replace. Thus the reduction of energy consumption for end-to-end data rate and maximization of network lifetime have become chief research concern. In recent years, many techniques have been proposed for improving the energy efficiency in wireless sensor networks. Among these techniques, V-MIMO technique has been considered as one of the effective ways to save energy. In WSN a node has two communication related energy consuming roles, one role is to serve as a packet generating source node (SN), and the other role is to relay packets for the other nodes, which are both energy consuming operations. This is why the energy minimization is required to extend the lifetime of the WSN.

T. Gui in 2016 [39] studied the mechanism of SMO in the field of WSNs in the paper “A Novel Cluster-based Routing Protocol Wireless Sensor Networks using Spider Monkey Optimization.” The study additionally showed the change in traditional routing protocols in term of low-energy consumption and system quality of the network. SMO-C protocol suggested in the paper worked for Wireless sensor networks to minimize global energy consumption.

Wireless sensor nodes consists of three pars first one is wireless means without wires second one is sensor mean our nodes or data sensing process third and last one is networks means connection of these nodes to form a network to communicate and report data. When we concern its quality of service it may become too wide part of research so we are promised to work only in energy efficient protocols. The reason behind wireless

A wireless sensor network (WSN) is a computer network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations.[1] The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. However, wireless sensor networks are now used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation, and traffic control.[1][2]

At present, Wireless Sensor Network has becoming a hot technological topic with the development of computer science and wireless communication technology, wireless sensor network (WSNs) is a system shaped by a substantial number of sensor nodes, each one furnished with sensors to recognize physical phenomena, for example, heat, light, movement, or sound. Utilizing diverse sensors, WSNs can be implemented to backing numerous applications including security, diversion, mechanization, mechanical checking, and open utilities also state management. however, numerous WSN gadgets have serious asset demands as far as vitality, computation, and memory, brought about by a need to cutoff the expense of the substantial number of gadgets needed for some applications and by organization

Also, we consider this connected WSN as 1-connected. Depends on the different connectivity/failure resilience requirement, the stronger types of connectivity need to be considered, i.e., k-connectivity. Based on the definition of connectivity in the graph theory, which is “it asks for the minimum number of elements (nodes or edges) that need to be removed to disconnect the remaining nodes from each other”, k-connectivity means that the network can keep its connectivity by removing at most k-1 nodes from itself. In this case, a network with k-connectivity and k>1 has much better fault tolerance/resilience than a network with only 1-connectivity. Since the nodes in a WSN could be failed randomly which are caused by battery depletion or wireless interference, preserving the k-connectivity is critical for WSN and could extend the life time of a WSN. Li et al. [6] provided a good survey on connectivity and coverage in WSNs.