Energy Management in Wireless Sensor Network

In recent years, the number of wireless sensor network deployments for real life applications has rapidly increased. Still, the energy problem remains one of the major barriers somehow preventing the complete exploitation of this technology. Sensor nodes are typically powered by batteries with a limited lifetime and, even when additional energy can be harvested from the external environment (e.g., through solar cells or piezo-electric mechanisms), it remains a limited resource to be consumed judiciously. Efficient energy management is thus a key requirement for a credible design of a wireless sensor network. Wireless sensor networks find great applications in radiation levels control, noise pollution control, atmospheric pollution control, structural health monitoring and smart vehicle parking. All sensors present in wireless sensor network are battery operated devices which have limited battery power. After the deployment of sensor devices it is impossible to replace each and every battery present in the network. So energy conservation must be taken. In this paper we proposed an energy efficient dynamic power management technique which can reduce power consumed by each sensor node by shutting down some components of sensors according to our algorithm which yields better savings and enhanced life time.

In many applications of the WSN, the major challenge is efficient consumption of energy by the nodes, thereby leading to the methods for optimized route management, congestion control mechanisms, and better traffic allocation. The proposed aim of this chapter is to demonstrate the method of combining clustering and traffic allocation protocol to achieve load balancing that increases the network lifetime. If the idea behind clustering of nodes is to evenly distribute the load on all discovered paths which will lead to balanced energy consumption at each node, in general; then, that behind combining it with traffic allocation protocol is to effectively smooth out the traffic evenly on all discovered routes. The protocol comprises of three steps, namely, initialization of network, clustering, and traffic allocation. The clustering phase will select the cluster head on the basis of the nodes’ energy level, and the traffic allocation phase distributes the data packets according to the capacity of the routes, in which process sensor nodes act as forwarding agents collecting the data packets arriving from the source node and forwarding it to nearest cluster head. To ensure the energy distribution is even in the network, average energy as well as consumed energy parameters are applied to optimize the process of cluster head selection. Traffic allocation strategy is also implemented to distribute the data packets evenly in all discovered paths, where traffic allocation consists of two parts: route weight assignment method and distributing the load evenly among the all discovered paths. In this chapter, the performance of the energy saving method is simulated and the results are encouraging. The network lifetime increases by 30%, in case of protocol, than what the traditional protocols used in WSN achieve. The energy consumption and saving methods also deliver the messages at double the rate than what traditional protocols achieve. However, WSN assisted IoT has various implementation issues related to energy and routing protocols. Still, the energy saving method is useful in many monitoring applications based on IoT and WSN. Sensor nodes are the major factor in construction of IoT based infrastructure and applications. The chapter will focus on the solutions for efficient energy management in WSN assisted IoT.

We consider a wireless sensor network (WSN) tasked with tracking a process using a set of distributed nodes. Here multiple remote sensor nodes estimate the physical process (viz., a moving object) and transmit quantized estimates to a fusion center for processing. At the fusion node a BLUE (best linear unbiased estimation) approach is used to combine the sensor estimates and to create a final estimate of the state. In this framework the uncertainty of the overall estimate is derived and shown to depend on the individual sensor transmit energy and quantization levels, as well as the Kalman tracker uncertainty at the node. Since power and bandwidth are critically constrained resources in battery operated sensor nodes, we attempt to quantify the trade-off between the lifetime of the network and the estimation quality over time. Three different convex formulations of the underlying nonconvex mixed integer nonlinear optimization problem are presented. Unlike previous work this effort incorporates the operating state of the nodes into the decisions of the optimum bits and the transmission power levels based on a heuristic. Simulation results for all formulations demonstrate the quality of the state estimate as well as the extended lifetime of the WSN.

In the last few years, interest in wireless sensor networks has increased considerably. These networks can be useful for a large number of applications, including habitat monitoring, structural health monitoring, pipeline monitoring, transportation, precision agriculture, supply chain management, and many more. Typically, a wireless sensor network consists of a large number of simple nodes which operate with exhaustible batteries, unattended. Manual replacement or recharging the batteries is not an easy or desirable task. Hence, how energy is utilized by the various hardware subsystems of individual nodes directly affects the scope and usefulness of the entire network. This paper provides a comprehensive assessment of state-of-the-art of dynamic power management (DPM) in wireless sensor networks. It investigates aspects of power dissipation in a node and analyses the strength and limitations of selective switching, dynamic frequency, and voltage scaling.

A common challenge in the implementation of Internet of Things (IOT) Wireless Sensor Networks (WSN) is that the sensor nodes are known to be power hungry devices. The energy stored in the batteries which power up these sensor nodes deplete quickly especially when more data is transmitted to the cloud or when multiple sensors are attached to a single sensor node. In the context of flood and environmental monitoring, increasing the number of sensor nodes in a Wireless Sensor Network is desirable in order to increase the spatial resolution of the data and hence achieve better representation about rising water levels and overall water quality in a particular city. However, having more sensor nodes in a Wireless Sensor Network results in more challenges for the power supply management of the overall Wireless Sensor Network. A drawback of the sensor nodes which are usually powered by Lithium Ion batteries is that there is a limited number of cycles in which a battery can be charged and discharged before the battery is considered to be fully degraded and therefore methods which can lengthen the duration of each charging and discharging cycle will be useful to increase the overall battery longevity. In this paper, a method which combines solar energy harvesting together with a fuzzy logic based algorithm for adaptive sampling is proposed in order to achieve a continuous source of energy for the sensor nodes and also increase the duration between each charging and discharging cycle resulting in batteries which can last for a longer duration. The developed sensor nodes have been deployed to measure river water levels and dissolved oxygen.

Mobile wireless sensor networks play a vital role in today’s real world applications in which the sensor nodes are related to the mobile network. Mobile wireless sensor networks are much added in resourceful than stagnant wireless sensor networks as the sensor nodes there in can be organize in any situation and deal with rapid topology changes. Secure power management is effective during an inside attack or other malicious attacks. It then efficiently turns the wireless sensor network nodes into inactive mode when they are unused and stir them up when required. To providing robust secure power management in wireless sensor networks. We proposed Efficient Secure Management Technique (ESMT) in mobile wireless sensor network. This scheme sends data efficiently across the network using routing strategy along with efficient security management.

Because of its huge scale and restrictions on scope of communication, a Wireless Sensor Network (WSN) generally depends on multi-hop transmissions to transmit a data packet along a succession of storage nodes. It is of key significance to estimate the forwarding nature of multi-hop routes and such data might be used in developing dynamic and economical routing methodologies. Objective: To develop a scheme which eases the determination of Cluster Heads (CH) in a WSN, which inturn would enhances the quality of path management. Analysis: Existing measurements for the most part, concentrate on evaluating the connection link performance among the sensor nodes while ignoring the forwarding abilities inside them. Recent studies demonstrate that the nature of forwarding inside every wireless sensor node is equally essential component that adds to the path quality in data transmission. Methodology: We propose a methodology to reduce the overhead by developing an efficient scheme for selecting a Cluster Head (CH) in each cluster to represent all the other nodes in the cluster. This CH will be responsible for representing the entire Cluster viz., To determine the routing path ,To detect the malicious nodes (Attacker Node) in each cluster, etc., which eventually eases the task of determining the forwarding path, in other words, enhancing the forwarding path quality. Findings/Improvements: A stand-alone application had been developed for this proposed system in Eclispe IDE and the results we obtained has proved that the proposed methodology ,when applied can achieve the required forwarding path quality management in WSN by evaluating both the connection link performance and also the forwarding abilities inside a sensor node.

With the rapid development of information technology, wireless sensor network(WSN) gradually permeates all walks of life. However, WSN is always out of work because of too much energy consumption on certain sensor nodes. This paper presents a new protocol based on parallel charged system search(PCSS) algorithm to balance energy consumption during WSN transmission. Firstly, a novel optimization algorithm named PCSS is presented based on two communication strategies with different conditions. First experiments on 10 benchmark functions in different dimensions demonstrate that the PCSS shows an excellent ability of convergency compared to CSS and PSO and the advantage of PCSS in quickly finding optimum is more obvious with the increasing of dimension. After that, a new clustering model based on PCSS(PCSS-C) is introduced to update cluster heads dynamically according to a designed fitness function, another experimental results illustrate that the proposed protocol is superior to low-energy adaptive clustering hierarchy, LEACH-centralized, and hybrid energy-efficient distributed clustering.

Wireless sensor networks (WSNs) are widely deployed in many fields nowadays. However, the limited energy storage ability of sensor nodes causes great detrimental effect on the performance of the WSNs. Energy harvesting has been proved as one of the promising ways to replenish the energy constrained sensors for prolonging WSNs lifetime. Another exacerbated problem introduced by non-uniform energy distribution and nonoptimal energy management in WSNs also causes great challenge to WSNs. For realizing the efficient energy utilization and improving the performance of energy harvesting enabled WSNs, two transmission management schemes, Markov decision based packets aggregation allocation (MDPA) and partial Kalman filter with best-match aggregation (PKBPA), are proposed respectively in this paper. MDPA focuses on the node's own energy capabilities to decide the packets number for aggregation, while PKBPA decides the number of packets to be aggregated and transmitted based on the estimated energy state information of the node and its next hop. Simulation results show that the PKBPA scheme outperforms other schemes, especially when the energy is insufficient for the packets transmission.

Energy constraints in a wireless sensor network are crucial issues critically affecting the network lifetime and connectivity. To realize true energy saving in a wireless environment, the time varying property of the wireless channel should also be taken into account. Unfortunately, this factor has long been ignored in most existing state-of-the-art energy saving protocols. Neglecting the effects of varying channel quality can lead to an unnecessary waste of precious battery resources, and, in turn, can result in the rapid depletion of sensor energy and partitioning of the network. In this paper, we propose a channel adaptive energy management protocol, called CAEM, that can exploit this time varying nature of the wireless link. Specifically, CAEM leverages on the synergistically cross-layer interaction between physical and MAC layers. Thus, each sensor node can intelligently access the wireless medium according to the current wireless link quality and the predicted traffic load, to realize an efficient utilization of the energy. Extensive simulation results indicate that CAEM can achieve as much as 40% reduction in energy dissipation compared with traditional protocols without channel adaptation.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Developments in last decade in the field of electronics & communication, computer science and information technology field have resulted in the new technology paradigm, Wireless Sensor Networks. WSN is becoming increasingly popular due to their wide range of applications. For successful adaption of WSN in real field applications, there are certain issues which need to be addressed particularly the issue of energy consumption associated with sensor nodes. This limitation combined with node deployment has added many challenges to design and management of wireless sensor networks. The WSNs are involved in applications such as remote environment monitoring, event tracking in areas where providing electrical energy is very difficult. Moreover, the battery fed sensor nodes have very small energy associated with them, so preserving that energy and maximizing the usage of that energy is most critical issue in WSNs. Here, we present a survey of various energy optimization and lifetime enhancement techniques in wireless sensor networks and makes the reader aware about various energy saving techniques in WSN. They are analyzed from various points of view: Device hardware, Transmission, MAC protocols and Routing protocols.

XML is the defacto standard for data exchange applications like those in the WWW. However, due to the limited hardware resources, wireless sensor networks abstain from using verbose data formats like XML. Nevertheless, XML as a standardized data exchange format in wireless sensor networks is a means to support more complex data management and heterogeneous networks. Moreover, XML is a key feature towards service-oriented sensor networks that exchange structured information by using SOAP. Recent work has shown that XML can be compressed to meet the general hardware restrictions of sensor nodes while still supporting updates. In this work we outline the vision and benefits of XML usage in wireless sensor networks, show how to evaluate XML queries in wireless sensor networks and how query results can be compressed to lower the comunication overhead. We therefore present an XPath engine on updateable compressed XML data for sensor nodes and an experimental evaluation showing that the performance of our XPath engine fulfills the requirements of today's applications even on sensor nodes.

Wireless sensor networks (WSNs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSNs. In particular, quality-of-service (QoS) management in WSNs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a logic for QoS management (LFQMS) scheme for WSNs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSNs. It utilizes a logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the sensor. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The (LFQMS) has the advantages of generality, scalability, and simplicity. Simulation results show that the (LFQMS) can provide WSNs with QoS support.

Wireless sensor networks have garnered considerable attention recently. Networks typically have many sensor nodes, and are used in commercial, medical, scientific, and military applications for sensing and monitoring the physical world. Many researchers have attempted to improve wireless sensor network management efficiency. A Simple Network Management Protocol (SNMP)-based sensor network management system was developed that is a convenient and effective way for managers to monitor and control sensor network operations. This paper proposes a novel WSNManagement system that can show the connections stated of relationships among sensor nodes and can be used for monitoring, collecting, and analyzing information obtained by wireless sensor networks. The proposed network management system uses collected information for system configuration. The function of performance analysis facilitates convenient management of sensors. Experimental results show that the proposed method enhances the alive rate of an overall sensor node system, reduces the packet lost rate by roughly 5%, and reduces delay time by roughly 0.2 seconds. Performance analysis demonstrates that the proposed system is effective for wireless sensor network management.