A uniform airdrop deployment method for large-scale wireless sensor networks

Node placement for connected target coverage in wireless sensor networks with dynamic sinks

Wireless sensor networks area collection of a large number of unattended sensor nodes. The sensor nodes are dynamically deployed in the region. In wireless sensor networks, the unlicensed sensor nodes use their temporarily available vacant licensed channels or spectrum band in a telecommunication network. The many open challenges with issues such as adaptive in self-organisation, fault-tolerance and network scalability with cooperative spectrum sensing and sharing are identifying new challenges in wireless sensor networks. In this paper, we proposed the bio-inspired adaptive routing algorithm for cognitive radio wireless sensor networks. The utilisation of the foraging approach of ANTs and BEEs for making the decision of using the available spectrum sensing and sharing of channels over multi-hops communication, provides the method to utilise idle channel and decrease the delay for communication in wireless sensor network. In this paper, we design the algorithm to solve the issue of wireless sensor network and performance of classical routing protocol LEACH and cognitive radio sensor networks (CRSN) evaluates the parameters which proof the self-organisation routing algorithm.

Wireless Sensor Network (WSN) most popular area of research where lots of work done are in this field. Energy efficiency is one of the most focusing areas because the lifetime of the network is the most common issue. Many algorithms work for cluster and these algorithms form cluster either distance or neighbor node. In our study, we focus on node deployment approach and after that, we focus on routing algorithm. In the Wireless Sensor Network, the node placement is the essential part for the proper communication between the sensor nodes and Base Station (BS). For better communication nodes should be aware of their own or neighbor node’s location. Better optimization of resources and performance improvement are the main concern for the Wireless Sensor Network. Optimal techniques should be utilized to place the nodes at the best possible locations for achieving the desired goal. For node placement, flower pollination optimization is used to generate the better result. Base Station is responsible for the communication of nodes with each other and it should be reachable to nodes. For this Region of Interest (RoI) is helpful to choose the best location. Placement of Base Station in the middle is the suitable place for the static nodes deployment and there should be another strategy for the dynamic environment. Nodes should be connected to each other for the transmission of data from the source to Base Station properly. From the MATLAB simulation, it has been shown that the proposed methodology improves the network performance in terms of dead nodes, energy and the various packets sent to Base Station.

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.

This paper presents the development of propagation models for wireless sensor networks for landslide management systems. Measurements of path loss in potential areas of landslide occurrence in Thailand were set up. The effect of the vegetation and mountain terrain in the particular area was therefore taken into account regarding the measured path loss. The measurement was carried out with short-range transmission/reception at 2400 MHz corresponding to IEEE 802.15.4 wireless sensor networks. The measurement setup was divided into two main cases, namely, the transmitting and receiving antennas installed on the ground and 1-m high above the ground. The measurement results are shown in this paper and used to develop propagation models suitable for operation of short-range wireless sensor networks of landslide management systems. The propagation model developed for the first case was achieved by fitting the averaged experimental data by the log-normal model plus the standard deviation. For the second case, the model was derived from the ray tracing theory. The mountain-side reflection path was added into the model which contained the reflection coefficient defined for the soil property. Furthermore, the resulting propagation models were employed in order to realistically evaluate the performance of wireless sensor networks via simulations which were conducted by using Castalia. In the simulations, the sensor nodes were placed as deterministic and random distributions within square simulated networks. The comparison between the results obtained from the deterministic and random distributions are discussed.

Wireless sensor networks can be deployed in normal as well as rough environment for data gathering fashion. Sensor nodes are more easy to failure because of their battery sources are irreplaceable. In Wireless Sensor networks (WSN), the sensors near to the sink will drain out of their energy first, resulting in holes in the WSNs. An important base for energy efficient routing technique in wireless sensor networks is the formation of energy holes near the sink. In this paper we propose Adaptive Tree Based Sink Relocation (ATBR) model. The proposed mechanism uses information related to residual battery energy of sensor nodes to adaptively adjust the routing tree and relocating scheme for the sink.

Various communication services have been provided. They include environmental monitoring and/or control, ad-hoc communication between mobile nodes, and inter-vehicle communication in intelligent transport systems. As a means of facilitating the above advanced communication services, autonomous decentralized networks, such as wireless sensor networks (Akyildiz et al., 2002; Rajagopalan & Varshney, 2006), mobile ad-hoc networks (Perkins & Royer, 1999; Johnson et al., 2003; Clausen & Jaquet, 2003; Ogier et al., 2003), and wireless mesh networks (Yamamoto et al., 2009), have been intensively researched with great interests. Especially, a wireless sensor network, which is a key network to construct ubiquitous information environments, has great potential as a means of realizing a wide range of applications, such as natural environmental monitoring, environmental control in residential spaces or plants, object tracking, and precision agriculture (Akyildiz et al., 2002). Recently, there is growing expectation for a new network service by a wireless sensor network consisting of a lot of static sensor nodes arranged in a service area and a few mobile robots as a result of the strong desire for the development of advanced systems that can flexibly function in dynamically changing environments (Matsumoto et al., 2009). In this chapter, a large scale and dense wireless sensor network made up of many static sensor nodes with global positioning system, which is a representative network to actualize the above-mentioned sensor applications, is assumed. In a large scale and dense wireless sensor network, generally, hundreds or thousands of static sensor nodes limited resources, which are compact and inexpensive, are placed in a service area, and sensing data of each node is gathered to a sink node by inter-node wireless multi-hop communication. Each sensor node consists of a sensing function to measure the status (temperature, humidity, motion, etc.) of an observation point or object, a limited function of information processing, and a simplified wireless communication function, and it generally operates on a resource with a limited power-supply capacity such as a battery. Therefore, a data gathering scheme and/or a routing protocol capable of meeting the following requirements is mainly needed to prolong the lifetime of a large scale and dense wireless sensor network composed of hundreds or thousands of static sensor nodes limited resources. 1. Efficiency of data gathering 2. Balance of communication load among sensor nodes 3. Adaptability to network topology changes

Now a days, it is essential, to improve the security including energy efficiency in various emerging application under WSN. To keep this in mind, a WDT node based sensor node master operations in wireless sensor network method is proposed. In this approach, network consists of random node deployment of sensor nodes and it is partitioned into levels. WDT is a watch dog timer based node acts as a station keeping node. In each level there is main selection and redundant selection of WDT node is considered. At a time one node is taken to monitor the other nodes in the level. By this all node health status is keep intact in different levels by the help of status word. Master operations includes, node information through status word, routing tree discovery for path used for data communication, efficient encryption technique in the path from source to sink via Key Distribution Centre(KDC). All this operations is carried out by WDT node and sensor nodes used in the path. The proposed approach also tells, how effectively energy level is maintained in the network. By implementing the WDT algorithm, routing tree and secured key based encryption methods the overall throughput, packet delivery ratio (PDR), End-to-End delay and energy may effectively manage.

Device deployment plays a key role in the performance of any large-scale wireless sensor network (WSN) application. WSN device deployment (i.e. the numbers and positions of the devices) must consider several design factors, viz. coverage, connectivity, lifetime, etc. However, connectivity remains the most fundamental factor especially in a large scale harsh environment. In this paper, we explore the problem of relay node (RN) placement in 3D forestry space. We formulate a generalized RN deployment optimization problem aimed at maximizing the network connectivity with constraints on RNs count. We investigate how the number of RNs can affect the connectivity of a WSN in a harsh environment. Based on quantitative analysis of such effects, the paper sets a threshold on the minimum number of required RNs.

A series of challenges exist in the process of sensor network research which can not be solved by one sensor node. Cooperative communication, based on the idea of spatial diversity, which makes different users or nodes in wireless network share their antennas or other network resources, is a promising technique toward improving performance of wireless sensor network significantly. This article adopts cooperation between nodes and opportunistic collaboration transmission strategy is proposed. Energy efficient power allocation algorithm in wireless sensor network is put forward to optimize performance of opportunistic cooperative transmission. Through the simulation, regardless of statistical feature of channel state of opportunistic node, opportunistic cooperation can effectively improve the performance of the system. Using energy efficient power allocation algorithm can effectively reduce energy consumption of the network and improve the network performance

Study of impacts of duty-cycle on overlapping multi-hop clustering in wireless sensor networks

Adaptive data aggregation scheme in clustered wireless sensor networks

In wireless sensor networks (WSNs) localization systems are required to provide position information of sensor nodes. Position information of sensor nodes with unknown physical positions is acquired with the help of beacons or anchors. Beacon placement is one of the prominent factors that shape the localization performance in sensor networks. Placing more beacons in a network is generally not a cost-effective idea. However, a carefully designed technique can increase the accuracy by placing a limited number of beacons. This paper presents a requirement based beacon placement algorithm to optimize number of beacons to cover the entire WSN. A novel algorithm for routing towards the mobile sink is also proposed, which utilizes the beacon nodes more than sensor nodes to route data towards a mobile sink. This strategy helps to reduce the energy consumption of the WSN and increase the network lifetime. Simulation result shows that the proposed beacon placement scheme is effective in a sensor network with high node density and also evaluates the proposed routing algorithm.

Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions, device monitoring, and collection of information. Due to limited energy resources available at SN, the primary issue is to present an energy-efficient framework and conserve the energy while constructing a route path along with each sensor node. However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routing with minimal energy consumption in WSN. This paper presents an energy-efficient routing system called Energy-aware Proportional Fairness Multi-user Routing (EPFMR) framework in WSN. EPFMR is deployed in the WSN environment using the instance time. The request time sent for the route discovery is the foremost step designed in the EPFMR framework to reduce the energy consumption rate. The proportional fairness routing in WSN selects the best route path for the packet flow based on the relationship between the periods of requests between different SN. Route path discovered for packet flow also measure energy on multi-user route path using the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR develops node dependent energy-efficient localized route path, improving the throughput. The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing, Throughput, RST, node density and average energy per packet in WSN. The Route Searching Time (RST) is reduced using the Boltzmann Distribution (BD), and as a result, the energy is minimized on multi-user WSN. Finally, GIFM applies an instance time difference-based route searching on WSN to attain an optimal energy minimization system. Experimental analysis shows that the EPFMR framework can reduce the RST by 23.47% and improve the throughput by 6.79% compared with the state-of-the-art works.

Traditional data acquisition methods for wireless sensor networks (WSNs) require all sensor nodes to transmit at least once to the sink node to obtain a full view of the network. In this paper we present Disagree, a different data acquisition method to retrieve data in WSNs that works in the opposite way as compared with traditional methods. In Disagree, only nodes that do not satisfy an assertion are required to transmit data back to the sink node in order to obtain a complete view of the network. We show that this behavior is the base of an energy-efficient way to gather all data. An important feature of Disagree is that it saves energy at the sensor level by exploiting data correlation. Rather than requesting explicit data readings from all sensor nodes, Disagree estimates the readings from sensor nodes that did not respond to the assertion. As a result of this policy, Disagree can obtain a view of the sensing field with different levels of resolution involving the transmission of only a subset of the sensor nodes by exploiting spatial data redundancy. We implemented Disagree in NS-2 network simulator and results indicate Disagree can significantly reduce the percentage of nodes replying to queries compared with flat and a cluster head based approaches.