scholarly journals Maximizing Data Extraction in Energy-Limited Sensor Networks

2005 ◽  
Vol 1 (1) ◽  
pp. 123-147 ◽  
Author(s):  
Narayanan Sadagopan ◽  
Bhaskar Krishnamachari
Keyword(s):  
Sensor Networks ◽  
Approximation Algorithm ◽  
Data Extraction ◽  
Data Availability ◽  
Greedy Heuristic ◽  
Energy Awareness ◽  
Energy Aware ◽  
Iterative Approximation ◽  
Significant Difference ◽  
Exponential Metric

We examine the problem of maximizing data collection from an energy-limited store-and-extract wireless sensor network, which is analogous to the maximum lifetime problem of interest in continuous data-gathering sensor networks. One significant difference is that this problem requires attention to “data-awareness” in addition to “energy-awareness”. We formulate the maximum data extraction problem as a linear program and present a 1 + ω iterative approximation algorithm for it. As a practical distributed implementation we develop a faster greedy heuristic for this problem that uses an exponential metric based on the approximation algorithm. We then show through simulation results that the greedy heuristic incorporating this exponential metric performs near-optimally (within 1 to 10% of optimal, with low overhead) and significantly better than other energy aware routing approaches (developed mainly through intuition), particularly when nodes are heterogeneous in their energy and data availability.

scholarly journals Design and Performance Analysis of Energy Aware Routing Protocol for Delay Sensitive Applications for Wireless Sensor Networks

2013 ◽  
Vol 1 (2013) ◽  
pp. 46-53
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Selection Process ◽  
Optimal Path ◽  
Wireless Sensor ◽  
Energy Awareness ◽  
Energy Aware ◽  
Efficient Manner ◽  
Efficient Operation

Wireless Sensor Networks (WSNs) are emerging network technology with innumerable applications. But security and energy constraints reduce its successful deployments. The nodes in network are greatly involved in transmissions and other processing operations for maintenance other than establishing or handling a call. Due to limited processing ability, storage capacity and most importantly the available battery power of the nodes, it is required to minimize the transmission power and the amount of data transmitted, for efficient operation. This paper presents a power aware routing protocol designed for wireless sensor networks. The proposed routing protocol is an extended and enhanced version of Dynamic Source Routing protocol. It adds energy awareness to the existing implementation of DSR protocol. Energy metric is considered during route selection process to choose an optimal path in terms of overall energy of the nodes along the path, and “low energy notification” method is used during route maintenance process to increase the lifetime of the bridge nodes to avoid network partitioning. The performance of DSR protocol and Energy Aware DSR (EADSR) protocol are compared through NS2 simulation under different scenarios. In all the cases, it is seen that EADSR protocol out-performs DSR protocol by energy saving in efficient manner

QoS and Energy-Aware Routing for Wireless Sensor Networks

2010 ◽  
pp. 497-514
Author(s):  
Shanghong Peng ◽  
Simon X. Yang ◽  
Stefano Gregori
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Algorithm ◽  
Energy Utilization ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Energy Awareness ◽  
Energy Aware ◽  
Directed Diffusion ◽  
Energy Aware Routing

Quality of service (QoS) and energy awareness are key requirements for wireless sensor networks (WSNs), which entail considerable challenges due to constraints in network resources, such as energy, memory capacity, computation capability, and maximum data rate. Guaranteeing QoS becomes more and more challenging as the complexity of WSNs increases. This chapter firstly discusses challenges and existing solutions for providing QoS and energy awareness in WSNs. Then, a novel bio-inspired QoS and energy-aware routing algorithm is presented. Based on an ant colony optimization idea, it meets QoS requirements in an energy-aware fashion and, at the same time, balances the node energy utilization to maximize the network lifetime. Extensive simulation results under a variety of scenarios demonstrate the superior performance of the presented algorithm in terms of packet delivery rate, overhead, load balance, and delay, in comparison to a conventional directed diffusion routing algorithm.

scholarly journals AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4036 ◽  
Author(s):  
Muhammad K. Shahzad ◽  
S. M. Riazul Islam ◽  
Kyung-Sup Kwak ◽  
Lewis Nkenyereye
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Fitness Function ◽  
Dynamic Network ◽  
Key Distribution ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Awareness ◽  
Energy Aware

Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.

The minimum zone fitting and error evaluation for the logarithmic curve based on geometry optimization approximation algorithm

2019 ◽  
Vol 41 (15) ◽  
pp. 4380-4386
Author(s):  
Tu Xianping ◽  
Lei Xianqing ◽  
Ma Wensuo ◽  
Wang Xiaoyi ◽  
Hu Luqing ◽  
...  
Keyword(s):  
Approximation Algorithm ◽  
Evaluation Method ◽  
Geometry Optimization ◽  
Reference Points ◽  
Approximation Technique ◽  
Error Evaluation ◽  
Iterative Approximation ◽  
Logarithmic Curve ◽  
Normal Distance ◽  
Minimum Zone

The minimum zone fitting and error evaluation for the logarithmic curve has important applications. Based on geometry optimization approximation algorithm whilst considering geometric characteristics of logarithmic curves, a new fitting and error evaluation method for the logarithmic curve is presented. To this end, two feature points, to serve as reference, are chosen either from those located on the least squares logarithmic curve or from amongst measurement points. Four auxiliary points surrounding each of the two reference points are then arranged to resemble vertices of a square. Subsequently, based on these auxiliary points, a series of auxiliary logarithmic curves (16 curves) are constructed, and the normal distance and corresponding range of values between each measurement point and all auxiliary logarithmic curves are calculated. Finally, by means of an iterative approximation technique consisting of comparing, evaluating, and changing reference points; determining new auxiliary points; and constructing corresponding auxiliary logarithmic curves, minimum zone fitting and evaluation of logarithmic curve profile errors are implemented. The example results show that the logarithmic curve can be fitted, and its profile error can be evaluated effectively and precisely using the presented method.

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