Analytical model for life time in wirless sensor networks

Ijitee, 2019

Advanced Technologies such as Internet of Things, Machine Networking give rise to the deployment of autonomous Wireless Sensor Nodes. They are used for various domains namely battlefield monitoring, enemy detection and monitoring the environment change. These Wireless Sensor Nodes have the properties of low cost and high battery life. NL (Network Lifetime) is an important phase of Wireless Sensor Network (WSNs), in which the nodes can maintain sensing for a more amount of time. NL can be improved by use of multiple techniques namely Opportunistic Transmission, Scheduling of Timed Data Packets, Clustering of Nodes, Energy Harvesting and Connectivity. This paper provides the energy consumption computation, life time ratio definition and the overview of NL improvement techniques. The paper also presents brief review of the Destination based and Source based routing algorithm.

Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference, 2015

In the recent year's low power computing systems have gained popularity. Networks, which use low power computer systems and transmitted data by using wireless connection are called wireless sensor networks, which main task is to get the information from sensors and transmission network. Nowadays, the most topical researches pertaining to wireless sensor networks are grounded on the new optimization of structure of network transmission protocol, the routing optimization in transmission network, optimization of network structure, as a result of which the life circle of wireless network sensors is possible to increase. In the present article the methodology for determining the life circle of network is discussed. The approaches in detection of life circle pertaining to the important network nodes are described.

In wireless sensor Networks can be widely used for variety of applications, various requirements and characteristics such as environmental monitoring, habitat monitoring, health care, animal tracking and surveillance. A Wireless sensor network is a network which is a collection of sensor nodes which are self controlled through the radio links. Each node in the wireless sensor network has the ability of processing separately which contains numerous memory, Transceiver and power resources These detecting devices i.e. sensors are legally responsible for data transmission from source location to destination location. To introduce the important models for lifetime model, sensor failure model, energy consumption model, sleep/wakeup model, routing model and retransmission model in WSN, are developed to aid in the analysis of WSN lifetime. Energy efficiency is the essential criteria for network lifetime enhancement. Therefore, consumption of every limited resource must be considered. The sensor network should have a lifetime long enough to fulfill the application requirements.

euroasiapub.org

Wireless sensor network (WSN) consists of tiny sensor nodes with sensing, computation and wireless communication capabilities. Now days, it is finding wide applicability and increasing deployment, as it enables reliable monitoring and analysis of environment. Lifetime maximization relates with various factors such as throughput, end to end delay, lifetime parameter such as time, output, packet delivery rates, no. of nodes, nodes efficiency and operating frequency to operate and relate each parameter. Balancing energy consumption and prolonging network lifetime are open challenges in Wireless Sensor Networks. In the research field of WSNs how to reduce the energy consumption of WSN so that the lifetime of WSN can be prolonged is one of the hottest spots. In this paper we survey various methods to maximize the lifetime of wireless Sensor Networks highlighting their objective, features, complexity etc.

Wireless Sensor Networks have attracted the attention of researchers in the recent years. In this paper we present design, implementation and deployment of WSN using waspmote and network lifetime measurements. The network lifetime measured is compared with network lifetime estimation approach. The estimation results show similar trend and are very close with the network lifetime measurement

2008

Due to the limited energy resource in the network nodes, lifetime is a considerable issue in wireless sensor networks. When the network is randomly deployed, network lifetime exhibits a stochastic behaviour which may be strengthened by a random traffic generation scheme. This dissertation mainly deals with the lifetime study of such networks where a probabilistic approach is proposed to model the network lifetime. First, we perform the study for non-clustered networks and propose an optimal node distribution to prolong the network lifetime. Then, the analysis is extended to clustered networks resulting in a condition which determines whether clustering in the network is beneficial or not. Our results on the lifetime study are helpful in the network design step to adjust the network parameters appropriately. Moreover, we investigate the traffic load distribution over the nodes in a randomly deployed linear network. The outcome of this traffic study specifies where the network hot spot is located.

IEICE Transactions on Communications, 2005

A wireless sensor network is comprised of a large number of battery-limited sensor nodes communicating with unreliable radio links. The nodes are deployed in an ad hoc fashion and a reverse multicast tree is formed in the target domain. The sink node disseminates a query and collects responses from the sensors over pre-established links. Survivability in wireless sensor networks reflects the ability of the network to continue to detect events in the case of individual node failures. We present a sender initiated path switching algorithm that enables the immediate sender to change the packet's route dynamically when its parent on the reverse path is down. The overall effect of path switching on the survivability is analyzed as a measure of reliable event delivery. Using independent battery capacities, an analytical model of a multihop network is derived. The model is used to predict the maximum network lifetime in terms of total transmitted messages; which is in turn used to verify the correctness of our simulations. The results have revealed that dynamic path switching has a better performance than static multipath routing and salvaging schemes. It has also been shown that the proposed approach enhances reliability upto 30% in some topologies.

Wireless sensor networks (WSNs) are recently attracting a lot of interest due to their low cost. They are rapidly replacing the wired data acquisition systems due to the ability to use them in benign environments. However, WSNs suffer from many constraints, including low computation capability, small memory, susceptibility to physical capture, the lack of infrastructure and limited energy resources which imposes unique challenges. One of the methods to improve the performance of a WSN is to introduce multiple hops between the source and destination. In this paper, an attempt has been made to analyze the relationship between network lifetime and the energy consumption in a multi-hop wireless sensor network (WSN). Network lifetime can be defined as the maximum time for which the network is able to successfully transmit data from the source to the sink, using all possible alternate routes. It has been shown that as the network lifetime increases, the percentage energy consumption decreases with increase in the number of hops and attains a minimum at critical hops. After the critical hops, the energy consumption gradually increases due to increase in cumulative energy consumption of the intermediate nodes.

2008

In event-driven wireless sensor networks, the network lifetime has a random nature due to the randomness of data reporting. The lifetime is even more nondeterministic when sensors are also deployed randomly. The lifetime of such a network is influenced by node deployment, initial energy of sensors, packet generation model and the number of sensors. This work quantifies the effect of these parameters on the lifetime of randomly deployed event-driven networks. First, the lifetime of individual sensors are studied. Then, an analytical expression is obtained for the complementary cumulative density function of the network lifetime. Such an analysis can be used for choosing the network parameter and efficiently optimizing the network lifetime. The results of this work are obtained for both multi- hop and single-hop wireless sensor networks and are verified with computer simulation. The approaches of this paper are shown to be applicable to more general cases.

2007 41st Annual Conference on Information Sciences and Systems, 2007

The inherent many-to-one flow of traffic in Wireless Sensor Networks produces a skewed distribution of energy consumption rates leading to the early demise of those sensors that are critical to the ability of surviving nodes to communicate their readings to the data collection center. Numerous previous approaches aimed at balancing the consumption of energy in wireless networks are based on a linear programming-based minmax formulation that seeks to maximize the minimum lifetime of sensors in a network. However, this approach fails to provide a clear picture of the cost at which this optimization is achieved and focuses attention on a single sensor, the minimum lifetime sensor. This paper makes two contributions; 1) it puts forward a new understanding of sensor network lifetime based on statistical measures, mean and variance, of node power consumption rates that provides a more inclusive view of the consumption rates, and 2) it provides an optimal quadratic programming (QP) formulation that provides an upper bound on the lifetime under a given set of topological and energy budgetary constraints. Our results demonstrate that the QP formulation has the ability to provide a soft trade-off between the mean and variance of power consumption rates.