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Issue 2 • 1000133 J Inform Tech Softw Eng ISSN: 2165-7866 JITSE, an open access journal Levendovszky and Thai
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Wireless sensor networks include a large number
of wireless Sensor nodes to gather the information from its
environment. These sensor nodes for various applications are
usually designed to work in conditions where it may not be
possible to recharge or replace the batteries of the nodes. So the
energy is a very precious resource for sensor nodes and
communication overhead has to be minimized. All these
constraints make the design of data communication protocols a
challenging task. Recently, many routing protocols have been
developed for Wireless Sensor Networks to improve its
performance by overcoming constraints like Residual Energy,
Communication Time, Communication Overhead and Energy
Consumption. From the experimental studies it is revealed that
our approach provides better energy efficient routing scheme
when compared to L-PEDAP which outperforms the other
recent protocols like PEDAP, PEDAP-PA and EESR in terms of
Resource Utilization, Bandwidth and Communication
Reliability. Our approach provides a duty cycle scheduling
scheme so that the energy dissipation of the nodes could be
minimized. The simulation results shows that this approach also
provide a better Network Partition Time (NPT) and First Node
Failure (FNF).
In this paper we introduce a new approach for wireless sensor network power management which is based on In this new approach an intelligent analysis is used to process the structure of a wireless sensor network (WS and produce some information which can be used to improve the performance of WS management application. We applied our intelligent method to our previously proposed management approach which uses the concept of Multi-Agent systems for WS management and observed the improvement of the performance. Wireless sensor networks need to be managed in different ways; e.g. power consumption of each sensor, efficient data routing without redundancy, sensing and data sending interval control, etc. The random distribution of wireless sensors, numerous variables which affect WS operation and the uncertainty of different algorithms (such as sensors' self-localization) give a fuzzy nature to WS Considering this fuzzy nature and numerous details, a neural network is an ideal tool to be used to cover these details which are so hard to be explicitly discovered and modeled. In this paper we introduce our based approach which results in a more efficient routing path discovery and sensor power management. We define a set of attributes based on sensors' location and neighborhood and we use them as inputs of our neural network and the output of the neural network will be used as a factor in the route path discovery and power management. We designed a simulator based on our approach and observed the effect of our method on wireless sensor network lifetime and sensor power consumption which will be presented in this paper.
Wireless Sensor Networks utilize large numbers of Wireless Sensor nodes to forward information from source to destination. Wireless Sensor Nodes are battery-powered devices. Energy saving is always vital to maximize the lifetime of Wireless Sensor Network. Recently, there are many Routing Protocols have been designed and proposed for Wireless Sensor Networks to improve its performance in terms of Communication Time, Residual Energy and Energy Consumption and also these protocols addressed Reliability and Shortest path. In this paper, this research work has implemented various Routing Protocols namely Power Efficient Data Gathering and Aggregation Protocol (PEDAP), Power Efficient Data Gathering and Aggregation Protocol-Power Aware (PEDAP-PA), Energy Efficient Spanning Tree approach (EESR), and Localized-Power Efficient Data Gathering and Aggregation Protocol (L-PEDAP) are studied thoroughly. From the experimental results, it is revealed that L-PEDAP outperforms PEDAP, PEDAP-PA and EESR in terms of Bandwidth, Resource Utilization and Communication Reliability. However, L-PEDAP fails to minimize the Energy Consumption and Communication Time due to Dynamic Routing Mechanisms. The PEDAP achieves less Communication Time to forward packets to destination. However, it fails to achieve Power-Aware Reliable Communication. From this analysis, it is observed that for faster communication, PEDAP could be used and for reliable communication with high bandwidth utilization, L-PEDAP is the best Routing Protocol in Wireless Sensor Networks.
Energy consumption of communication is a key factor of the lifetimes of wireless sensor networks. This paper presents an energy-efficient routing protocol for wireless sensor network. In the protocol, each sensor node detects the distance between the base station and itself. Then, it calculates a tier ID in according to the distance. A lower tier ID indicates a shorter distance between the base station and the node. Nodes with higher tier IDs send data to their neighbors with lower tier IDs, where data is compressed and forwarded toward nodes of even lower tiers. Eventually the data reaches the nodes at the lowest tier, then the system selects a node sending data to the base station. Because long-distance communication between the base station and the node is energy-consuming, it will have its energy drained off faster than other nodes. The protocol employs a mechanism to shift the long-distance communication among all network nodes. Thus, energy consumption is evenly distributed among all network nodes.
Understanding the fundamental performance limits of wireless sensor networks is critical towards their appropriate deployment strategies. Both the data transmission rate and the lifetime of sensor networks can be constrained, due to interference among the transmissions and the limited energy source of the sensor. In addition to presenting the general results with respect to the maximum sustainable throughput of wireless sensor networks, this chapter focuses on the discussion of the energy-constrained fundamental limits with respect to the network throughput and lifetime. With an adequate deflnition of operational lifetimes, our asymptotic analysis shows that, with flxed node densities, operational lifetime of sensor networks decreases in the order of 1=n as the number of initially deployed nodes n grows. Even with renewable energy sources on each of the sensors (e.g., solar energy sources), our analysis concludes that the maximum sustainable throughput in energy-constrained sensor networks scales worse than the capacity based on interference among concurrent transmissions as long as the physical network size grows with n in the order greater than logn. In this case, when the number of nodes is su-ciently high, the energy-constrained network capacity dominates. 1. Introduction. When compared to other categories of wireless networks, wireless sensor networks possess two fundamental characteristics: multi-hop transmission and con- strained energy sources. First, since sensor nodes have limited transmission ranges and organize themselves in an ad hoc fashion, two wireless sensor nodes that can not reach each other directly rely on other sensor nodes to relay data between them. In general, data packets from the source node need to traverse multiple hops before they reach the desti- nation. Second, since sensors are usually small and inexpensive, they are assumed to have constrained energy sources, and any protocols to be deployed in sensor networks need to be aware of energy usage. These two characteristics have important implications to the fundamental performance limits of wireless sensor networks. With respect to the performance of wireless sensor networks, the data transmission capacity and the lifetime of the sensor networks are critical and in∞uential towards the design of optimal deployment strategies of these sensor networks. The fundamental limits of these two critical performance parameters lead to a few interesting open problems. First, what is the maximum sustainable throughput of the network? Second, what is the maximum lifetime of the network? These questions are usually considered given a set of parameters of the sensor network, and under the assumption that optimal network management is achievable. The set of parameters of the sensor network under consideration includes the number of sensor nodes in the network, as well as the area occupied by the sensor network. Issues relevant to network management usually includes packet routing, power management, and topology control. The answers to the questions previously asked are of great importance to both theo- retical and practical aspects of wireless sensor networking research. First, studies on the asymptotic behavior of network throughput and lifetime with respect to the network size and area provide insights pertinent to the network scalability and feasibility of deploying large- scale wireless sensor networks. Second, the results with respect to the maximum network throughput and lifetime ofiers important guidance to research on the network management
Recent developments in processor, memory and radio technology have enabled wireless sensor networks which are deployed to collect useful information from an area of interest. The sensed data must be gathered and transmitted to a base station where it is further processed for end-user queries. Since the network consists of low-cost nodes with limited battery power, power efficient methods must be employed for data gathering and aggregation in order to achieve long network lifetimes.In an environment where in a round of communication each of the sensor nodes has data to send to a base station, it is important to minimize the total energy consumed by the system in a round so that the system lifetime is maximized. With the use of data fusion and aggregation techniques, while minimizing the total energy per round, if power consumption per node can be balanced as well, a near optimal data gathering and routing scheme can be achieved in terms of network lifetime.So far, besides the conventional protocol of direct transmission, two elegant protocols called LEACH and PEGASIS have been proposed to maximize the lifetime of a sensor network. In this paper, we propose two new algorithms under name PEDAP (Power Efficient Data gathering and Aggregation Protocol), which are near optimal minimum spanning tree based routing schemes, where one of them is the power-aware version of the other. Our simulation results show that our algorithms perform well both in systems where base station is far away from and where it is in the center of the field. PEDAP achieves between 4x to 20x improvement in network lifetime compared with LEACH, and about three times improvement compared with PEGASIS.
In this paper, we introduce two fading-aware reliability based routing algorithms for wireless sensor networks (WSNs) with lossy radio links. The proposed algorithms are able to find optimal multi-hop paths in polynomial complexity, over lossy links, which are modeled by using standard fading models (e.g. Rayleigh and Rice fading). These algorithms minimize the energy consumption and ensure reliable packet transmission to the base station (BS) at the same time. A reliable path is defined in terms of successful packet transfer to the BS despite the lossy links. More precisely, the probability of correct reception of the packet at the BS must exceed a predefined threshold. The first algorithm minimizes the total energy consumption sending a packet over the selected path to the BS. On the other hand, the second algorithm selects a path which maximizes the minimum remaining energy on the node closest to exhaustion and, as a result, balances the energy consumption yielding high longevity. In both cases, reliable and energy efficient packet forwarding in WSN can be reduced to a constrained optimization problem. By using a specific link metrics, these problems can then be mapped into shortest path problems solved in polynomial time. Thus the obtained results ensure the selection of reliable paths which also guarantee minimum energy consumption in real time.