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MLO: Multi-level Optimization to Enhance the Network Lifetime in Large Scale WSN
Abstract
Ensuring the optimal energy efficiency is one of the unsolved problems in Wireless Sensor Networks (WSN) owing to varying power requirements of hardware components of sensors, massive load on data aggregation and less efficient energy aware routing policies. Hence, this paper describes a multi-level optimization (MLO) for the purpose of enhancing the network lifetime in WSN. The techniques discussed in this paper uses graph theory for formulating data aggregation and uses first order radio-energy model for evolving up with novel routing condition to attain less depletion of energy while performing data aggregation. The outcome of the study was compared with standard LEACH algorithm with respect to energy consumption and time to find MLO is better than LEACH.
... Figure 1 shows the architecture of proposed MeMLO which addresses the problems of energy dissipation in wireless sensor network. The proposed MeMLO is a continuation of our prior techniques [21,22]. The next section discusses about the research methodology adopted for developing MeMLO. ...
... The design and development of our prior MLO algorithm [22] was total based on analytical background. The target was to use the optimization potential of MLO algorithm for accomplishing an enhanced network lifetime with efficient clustering mechanism. ...
... location, energy, neighborhood, referential position, aggregator node-sink node distance, and different aggregator node distance. The study outcome also exhibits better network lifetime and hence both EO-RTD [21] and MLO [22] is an energy efficient clustering process. ...
The paper presents a technique called as Mobility-Enabled Multi Level Optimization (MeMLO) that addressing the existing problem of clustering in wireless sensor net-work (WSN). The technique enables selection of aggregator node based on multiple optimization attribute which gives better decision capability to the clustering mechanism by choosing the best aggregator node. The outcome of the study shows MeMLO is highly capable of minimizing the halt time of mobile node that significantly lowers the transmit power of aggregator node. The simulation outcome shows negligible computational complexity, faster response time, and highly energy efficient for large scale WSN for longer simulation rounds as compared to conventional LEACH algorithm.
... Figure 1 shows the architecture of proposed MeMLO which addresses the problems of energy dissipation in wireless sensor network. The proposed MeMLO is a continuation of our prior techniques [21][22]. The next section discusses about the research methodology adopted for developing MeMLO. ...
... The design and development of our prior MLO algorithm [22] was total based on analytical background. The target was to use the optimization potential of MLO algorithm for accomplishing an enhanced network lifetime with efficient clustering mechanism. ...
... location, energy, neighborhood, referential position, aggregator node-sink node distance, and different aggregator node distance. The study outcome also exhibits better network lifetime and hence both EO-RTD [21] and MLO [22] is an energy efficient clustering process. ...
The paper presents a technique called as Mobility-enabled Multi Level Optimization (MeMLO) that addressing the existing problem of clustering in wireless sensor net-work (WSN). The technique enables selection of aggregator node based on multiple optimi-zation attribute which gives better decision capability to the clustering mechanism by choosing the best aggregator node. The outcome of the study shows MeMLO is highly capable of minimizing the halt time of mobile node that significantly lowers the transmit power of aggregator node. The simulation outcome shows negligible computational com-plexity, faster response time, and highly energy efficient for large scale WSN for longer simulation rounds as compared to conventional LEACH algorithm. Copyright © 2017 Institute of Advanced Engineering and Science. All rights reserved.
... In a continuation of our prior studies [31], [32], [33], the proposed study introduces a technique where a multiple-level of optimization is carried out for the purpose of enhancing the network lifetime of wireless sensor network. The complete work has been carried out considering analytical methodology. ...
Although adoption pace of wireless sensor network has increased in recent times in many advance technologies of ubiquitous-ness, but still there are various open-end challenges associated with energy efficiencies among the sensor nodes till now. We reviewed the existing research approaches towards energy optimization techniques to explore significant problems. This paper introduces MOMEE i.e. Manifold Optimized Modeling of Energy Efficiency that offers novel clustering as well as novel energy optimized routing strategy. The proposed system uses analytical modeling methodology and is found to offer better resiliency against traffic bottleneck condition. The study outcome of MOMEE exhibits higher number of alive nodes, lower number of dead nodes, good residual energy, and better throughput as compared to existing energy efficient routing approaches in wireless sensor network.
The area of wireless sensor network (WSN) is shrouded with the issues of routing protocol that has been witnessed in the last few decade in the research community. One of the direct influences of routing protocol is the performance of data aggregation in WSN. This paper presents a novel routing protocol that performs a cost effective, reliable and robust routing mechanism in wireless sensor network with its highly compatibility with in-network data aggregation mechanism. A simulation study is performed Matlab, where along with proposed routing algorithm, two more conventional algorithm is chosen to perform the performance comparative analysis. The performance parameters selected for simulation study shows that the proposed routing protocol is robust against the conventional routing protocol with respect to efficiency and data delivery phenomenon.
A wireless sensor network (WSN) depends on miniaturized wireless sensor nodes that are deployed to monitor physical phenomena by communicating with each other with limited resources. The major factor to be tackled in the WSN is the network lifetime. A recent WSN routing protocol defined as secure real-time load distribution (SRTLD) uses broadcast packets to perform neighbor discovery and calculation at every hop while transferring data packets. Thus, it has high energy consumption. The proposed novel biological inspired self-organized secure autonomous routing protocol (BIOSARP) enhances SRTLD with an autonomous routing mechanism. In the BIOSARP mechanism, an optimal forwarding decision is obtained using improved ant colony optimization (IACO). In IACO, the pheromone value/probability is computed based on the end-to-end delay, remaining battery power, and link quality metrics. The proposed BIOSARP has been designed to reduce the broadcast and packet overhead in order to minimize the delay, packet loss, and power consumption in the WSN. In this paper, we present the architecture, implementation, and detailed outdoor experimental testbed results of the proposed BIOSARP. These results show that BIOSARP outperforms energy and delay ants algorithm, improved energy-efficient ant-based routing, and SRTLD in simulations and as well as in real testbed experimentation. The empirical study confirmed that BIOSARP offers better performance and can be practically implemented in the WSN applications for structural and environmental monitoring or battlefield surveillance.
Designing energy efficient and reliable routing protocols for mobility centric applications of wireless sensor network (WSN) such as wildlife monitoring, battlefield surveillance and health monitoring is a great challenge since topology of the network changes frequently. Existing cluster-based mobile routing protocols such as LEACH-Mobile, LEACH-Mobile-Enhanced and CBR-Mobile consider only the energy efficiency of the sensor nodes. However, reliability of routing protocols by incorporating fault tolerance scheme is significantly important to identify the failure of data link and sensor nodes and recover the transmission path. Most existing mobile routing protocols are not designed as fault tolerant. These protocols allocate extra timeslots using time division multiple access (TDMA) scheme to accommodate nodes that enter a cluster because of mobility and thus, increases end-to-end delay. Moreover, existing mobile routing protocols are not location aware and assume that sensor nodes know their coordinates. In this study the authors, we propose a location-aware and fault tolerant clustering protocol for mobile WSN (LFCP-MWSN) that is not only energy efficient but also reliable. LFCP-MWSN also incorporates a simple range free approach to localise sensor nodes during cluster formation and every time a sensor moves into another cluster. Simulation results show that LFCP-MWSN protocol has about 25-30% less network energy consumptions and slightly less end-to-end data transmission delay than the existing LEACH-Mobile and LEACH-Mobile-Enhanced protocols.
Traditional routing protocols are no longer suitable for the energy harvesting-wireless sensor networks (EH-WSN), which is powered by the energy harvested from environment instead of batteries. Rather than minimising the energy consumption and maximising the network lifetime, the main challenge in EH-WSN is to maximise its working performance under energy harvesting constraints. In this study, the authors propose a centralised power efficient routing algorithm energy harvesting genetic-based unequal clustering-optimal adaptive performance routing algorithm (EHGUC-OAPR) which contains two parts: (i) energy harvesting genetic-based unequal clustering algorithm EHGUC and (ii) optimal adaptive performance routing algorithm (OAPR). First, the base station (BS) uses EHGUC algorithm to form clusters of unequal size and select associated cluster heads, in which the clusters closer to the BS have smaller size. Then, the BS adopts OAPR algorithm to construct an optimal routing among each cluster heads. The numerical results show that EHGUC-OAPR is not only well applied to EH-WSN, but also has a great improvement in network energy balance and data delivery ratio.
Advances in Wireless Sensor Network Technology (WSN) have provided the
availability of small and low-cost sensor with capability of sensing various
types of physical and environmental conditions, data processing and wireless
communication. In WSN, the sensor nodes have a limited transmission range, and
their processing and storage capabilities as well as their energy resources are
limited. Triple Umpiring System (TUS) has already been proved its better
performance on Wireless Sensor Networks. Clustering technique provides an
effective way to prolong the lifetime of WSN. In this paper, we modified the Ad
hoc on demand Distance Vector Routing (AODV) by incorporating Signal to Noise
Ratio (SNR) based dynamic clustering. The proposed scheme Efficient and Secure
Routing Protocol for Wireless Sensor Networks through SNR based dynamic
Clustering mechanisms (ESRPSDC) can partition the nodes into clusters and
select the Cluster Head (CH) among the nodes based on the energy and Non
Cluster Head (NCH) nodes join with a specific CH based on SNR Values. Error
recovery has been implemented during Inter cluster routing itself in order to
avoid end-toend error recovery. Security has been achieved by isolating the
malicious nodes using sink based routing pattern analysis. Extensive
investigation studies using Global Mobile Simulator (GloMoSim) showed that this
Hybrid ESRP significantly improves the Energy efficiency and Packet Reception
Rate (PRR) compared to SNR unaware routing algorithms like Low Energy Aware
Adaptive Clustering Hierarchy (LEACH) and Power- Efficient Gathering in Sensor
Information Systems (PEGASIS).
Reliable communications are essential for most applications in wireless sensor networks (WSNs). In traditional approaches, the per-hop and end-to-end (E2E) recovery schemes are widely used. These schemes, however, suffer from low E2E success rate and poor energy efficiency in large-scale real environments. Through empirical studies, in this paper we identify three major problems that hinder the efficient and reliable communications. To address these problems, we propose a novel in-middle recovery scheme and realize it by designing and implementing a proliferation routing. Proliferation routing integrates three core technologies, namely, capability-based path finder, a randomized dispersity, and reproduction. Proliferation routing offers great flexibilities for transmissions. It cannot only be applied with any Medium Access Control (MAC) protocols and routing metrics, but also obtains a desired service quality (i.e., transmission success rate, energy cost, etc.) by controlling the system parameters. To demonstrate the effectiveness of proliferation routing, we thoroughly analyze its performance. We also conduct performance evaluations through implementation experiments as well as simulations. In a specific experimental setup, proliferation routing can increase the E2E transmission success rate up to 80 percent compared with the well-known hop-based routing and flooding.
Wireless sensor network (WSN) is composed by a large number of low-cost micro-sensors to collect and send various kinds of messages to base station (BS). Since the network consists of low-cost nodes with limited battery power and battery replacement is not an option for network with thousands of physically embedded nodes, energy efficient routing protocol must be employed to get long-life work time. We need not only to minimize total energy consumption but also to balance WSN load. Researchers have proposed many elegant protocols such as LEACH, HEED, PEGASIS, and PEDAP. In this paper, we propose General Self-Organization Tree-Based Energy-Balance routing protocol (GSTEB). GSTEB forms a self-organizing routing tree. For each round, BS assigns root node and broadcasts to all others, each node only using local information of its own and its neighbors to select its parent. These methods make GSTEB to be a dynamic and parallel protocol. Simulation results show that GSTEB has a better performance than other protocols on balancing energy consumption and prolonging lifetime of WSN. Moreover, GSTEB can be used in more applications.
One of the fundamental design challenges in designing a Wireless Sensor Network (WSN) is to maximize the network lifetime, as each sensor node of the network is equipped with a limited power battery. To overcome this challenge, different methods were developed in the last few years using such techniques as network protocols, data fusion algorithms using low power, energy efficient routing, and locating optimal sink position. This paper focuses on finding the optimal sink position. Relay nodes are introduced in conjunction with the sensor nodes to mitigate network geometric deficiencies since in most other approaches the sensor nodes close to the sink become heavily involved in data forwarding and, thus, their batteries are quickly depleted. A Particle Swarm Optimization (PSO) based algorithm is used to locate the optimal sink position with respect to those relay nodes to make the network more energy efficient. The relay nodes communicate with the sink instead of the sensor nodes. Tests show that this approach can save at least 40% of the energy and prolong the network lifetime.
A convergecast is a popular routing scheme in wireless sensor networks (WSNs) in which every sensor node periodically forwards measured data along configured routing paths to a base station (BS). Prolonging lifetimes in energy-limited WSNs is an important issue because the lifetime of a WSN influences on its quality and price. Low-energy adaptive clustering hierarchy (LEACH) was the first attempt at solving this lifetime problem in converge-cast WSNs, and it was followed by other solutions including power efficient gathering in sensor information systems (PEGASIS) and power efficient data gathering and aggregation protocol (PEDAP). Our solution — chain routing with even energy consumption (CREEC) — solves this problem by achieving longer average lifetimes using two strategies: i) Maximizing the fairness of energy distribution at every sensor node and ii) running a feedback mechanism that utilizes a preliminary simulation of energy consumption to save energy for depleted sensor nodes. Simulation results confirm that CREEC outperforms all previous solutions such as LEACH, PEGASIS, PEDAP, and PEDAP-power aware (PA) with respect to the first node death and the average lifetime. CREEC performs very well at all WSN sizes, BS distances and battery capacities with an increased convergecast delay.
Building wireless sensor networks: with Zig-Bee. X-Bee Arduino and Processing
- R Faludi