IMPROVING NETWORK LIFETIME BY MINIMIZING ENERGY HOLE PROBLEM IN WSN FOR THE APPLICATION OF IoT

The world today is at the Internet of Things (IoT) inflection point with more number of products adding to its intelligence system through a wide range of connectivity. Wireless sensor Networks (WSN) have been very useful in IoT application for gathering and processing of data to the end user. However, limited battery power and network lifetime are few of the major challenges in the designing process of any sensor network. One of those  is the Energy Hole Problem (EHP) that arises when the nodes nearer to the sink or base station die out early due to excess load as compared to other nodes that are far away. This breaks the connection of the network from the sink which results in shortening the lifetime of the network. In this paper, a trade-off is maintained between network lifetime and power requirement by implementing a sleep-awake mechanism.With the help of MATLAB simulations, it is found that after applying the mechanism, the network lifetime was extended to almost 300 and 700 rounds for TEEN and LEACH protocol respectively. The results will be beneficial for the design process in WSN for IoT application

Hybrid Heterogeneous Routing Scheme for Improved Network Performance in WSNs for Animal Tracking

Wireless Sensor Networks (WSNs) experiences several technical challenges such as limited energy, short transmission range, limited storage capacities, and limited computational capabilities. Moreover, the sensor nodes are deployed randomly and massively over an inaccessible or hostile region. Hence WSNs are vulnerable to adversaries and are usually operated in a dynamic and unreliable environment. Animal tracking using wireless sensors is one such application of WSN where power management plays a vital role. In this paper, an energy-efficient hybrid routing method is proposed that divides the whole network into smaller regions based on sensor location and chooses the routing scheme accordingly. The sensor network consists of a base station (BS) located at a distant place outside the network, and a relay node is placed inside the network for direct communications from nodes nearer to it. The nodes are further divided into two categories based on the supplied energy; such that the ones located far away from BS and relay have higher energy than the nodes nearer to them. The network performance of the proposed method is compared with protocols like LEACH, SEP, and SNRP, considering parameters like stability period, throughput and energy consumption. Simulation result shows that the proposed method outperforms other methods with better network performance

Residual Energy Based Cluster-head Selection in WSNs for IoT Application

Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. The paper focuses on an efficient cluster head election scheme that rotates the cluster head position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy and an optimum value of cluster heads to elect the next group of cluster heads for the network that suits for IoT applications such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the LEACH protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%

Hybrid Heterogeneous Routing Scheme for Improved Network Performance in WSNs for Animal Tracking

Wireless Sensor Networks (WSNs) experiences several technical challenges such as limited energy, short transmission range, limited storage capacities, and limited computational capabilities. Moreover, the sensor nodes are deployed randomly and massively over an inaccessible or hostile region. Hence, WSNs are vulnerable to adversaries and are usually operated in a dynamic and unreliable environment. Animal tracking using wireless sensors is one such application of WSN where power management plays a vital role. In this paper, an energy-efficient hybrid routing method is proposed that divides the whole network into smaller regions based on sensor location and chooses the routing scheme accordingly. The sensor network consists of a base station (BS) located at a distant place outside the network, and a relay node is placed inside the network for direct communications from nodes nearer to it. The nodes are further divided into two categories based on the supplied energy; such that the ones located far away from BS and relay have higher energy than the nodes nearer to them. The network performance of the proposed method is compared with protocols like LEACH, SEP, and SNRP, considering parameters like stability period, throughput and energy consumption. Simulation result shows that the proposed method outperforms other methods with better network performance

Biological control of the grapevine diseases ‘grey mold’ and ‘powdery mildew’ by <i>Bacillus</i> <span style="mso-bidi-font-family:"Times New Roman"; mso-fareast-language:FR" lang="EN-US">B27 and B29 strains<span style="mso-bidi-font-style:italic"> </span></span>

109-115<span style="mso-ansi-language:EN-US; mso-fareast-language:FR" lang="EN-US">Uncinula necator and Botrytis cinerea are the most destructive pathogens of the grapevine in Tunisia and elsewhere. We used <span style="mso-ansi-language:EN-US;mso-fareast-language: FR" lang="EN-US">two strains of Bacillus subtilis group, B27 and B29 to control powdery mildew and the grey mold disease of the grapevine. Green house experiments showed that B29 and B27 strains of the bacteria efficiently reduced the severity of powdery mildew up to 50% and 60%, respectively. Further, they decreased Botrytis cinerea development on grape leaf by 77% and 99%, respectively. The mode of action has been shown to be chitinolytic. These two bacteria showed significant production of total proteins discharged into the culture medium. Determination of some chitinolytic enzymes revealed the involvement of N-acetyl glucosaminidase (Nagase), the chitin-1,4-chitobiosidase (Biase) and endochitinase in degrading the mycelium of B. cinerea. </span

Energy-Efficient Routing for Greenhouse Monitoring Using Heterogeneous Sensor Networks

A suitable environment for the growth of plants is the Greenhouse, that needs to be monitored by a continuous collection of data related to temperature, carbon dioxide concentration, humidity, illumination intensity using sensors, preferably in a wireless sensor network (WSN). Demand initiates various challenges for diversified applications of WSN in the field of IoT (Internet of Things). Network design in IoT based WSN faces challenges like limited energy capacity, hardware resources, and unreliable environment. Issues like cost and complexity can be limited by using sensors that are heterogeneous in nature. Since replacing or recharging of nodes in action is not possible, heterogeneity in terms of energy can overcome crucial issues like energy and lifetime. In this paper, an energy efficient routing process is discussed that considers three different sensor node categories namely normal, intermediate and advanced nodes. Also, the basic cluster head (CH) selection threshold value is modified considering important parameters like initial and residual energy with an optimum number of CHs in the network. When compared with routing algorithms like LEACH (Low Energy Adaptive Clustering Hierarchy) and SEP (Stable Election Protocol), the proposed model performs better for metrics like throughput, network stability and network lifetime for various scenarios

CH Selection via Adaptive Threshold Design Aligned on Network Energy

Energy consumption in Wireless Sensor Networks (WSN) involving multiple sensor nodes is a crucial parameter in many applications like smart healthcare systems, home automation, environmental monitoring, and industrial use. Hence, an energy-efficient cluster-head (CH) selection strategy is imperative in a WSN to improve network performance. So to balance the harsh conditions in the network with fast changes in the energy dynamics, a novel energy-efficient adaptive fuzzy-based CH selection approach is projected. Extensive simulations exploited various real-time scenarios, such as varying the optimal position of the location of the base station and network energy. Additionally, the results showed an improved performance in the throughput (46%) and energy consumption (66%), which demonstrated the robustness and efficacy of the proposed model for the future designs of WSN applications

Residual Energy-Based Cluster-Head Selection in WSNs for IoT Application

Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%

I-SEP: An Improved Routing Protocol for Heterogeneous WSN for IoT-Based Environmental Monitoring

Wireless sensor networks (WSNs) is a virtual layer in the paradigm of the Internet of Things (IoT). It inter-relates information associated with the physical domain to the IoT drove computational systems. WSN provides an ubiquitous access to location, the status of different entities of the environment, and data acquisition for long-term IoT monitoring. Since energy is a major constraint in the design process of a WSN, recent advances have led to project various energy-efficient protocols. Routing of data involves energy expenditure in considerable amount. In recent times, various heuristic clustering protocols have been discussed to solve the purpose. This article is an improvement of the existing stable election protocol (SEP) that implements a threshold-based cluster head (CH) selection for a heterogeneous network. The threshold maintains uniform energy distribution between member and CH nodes. The sensor nodes are also categorized into three different types called normal, intermediate, and advanced depending on the initial energy supply to distribute the network load evenly. The simulation result shows that the proposed scheme outperforms SEP and DEEC protocols with an improvement of 300% in network lifetime and 56% in throughput

Identification and Characterization of Differentially Expressed Genes in Inferior and Superior Spikelets of Rice Cultivars with Contrasting Panicle-Compactness and Grain-Filling Properties

<div><p>Breeding programs for increasing spikelet number in rice have resulted in compactness of the panicle, accompanied by poor grain filling in inferior spikelets. Although the inefficient utilization of assimilate has been indicated as responsible for this poor grain filling, the underlying cause remains elusive. The current study utilized the suppression subtractive hybridization technique to identify 57 and 79 genes that overexpressed in the superior and inferior spikelets (with respect to each other), respectively, of the compact-panicle rice cultivar Mahalaxmi. Functional categorization of these differentially expressed genes revealed a marked metabolic difference between the spikelets according to their spatial location on the panicle. The expression of genes encoding seed storage proteins was dominant in inferior spikelets, whereas genes encoding regulatory proteins, such as serine-threonine kinase, zinc finger protein and E3 ligase, were highly expressed in superior spikelets. The expression patterns of these genes in the inferior and superior spikelets of Mahalaxmi were similar to those observed in another compact-panicle cultivar, OR-1918, but differed from those obtained in two lax-panicle cultivars, Upahar and Lalat. The results first suggest that the regulatory proteins abundantly expressed in the superior spikelets of compact-panicle cultivars and in both the superior and inferior spikelets of lax-panicle cultivars but poorly expressed in the inferior spikelets of compact-panicle cultivars promote grain filling. Second, the high expression of seed-storage proteins observed in the inferior spikelets of compact-panicle cultivars appears to inhibit the grain filling process. Third, the low expression of enzymes of the Krebs cycle in inferior spikelets compared with superior spikelets of compact-panicle cultivars is bound to lead to poor ATP generation in the former and consequently limit starch biosynthesis, an ATP-consuming process, resulting in poor grain filling.</p></div