A NOVEL HYBRID OPPORTUNISTIC SCALABLE ENERGY EFFICIENT ROUTING DESIGN FOR LOW POWER, LOSSY WIRELESS SENSOR NETWORKS
Jayavignesh Thyagarajan1 and Suganthi K1 , 1 Vellore Institute of Technology, Chennai Campus, India
Opportunistic Routing (OR) scheme increases the transmission reliability despite the lossy wireless radio links by exploiting the broadcast nature of the wireless medium. However, OR schemes in low power Wireless Sensor Network (WSN) leads to energy drain in constrained sensor nodes due to constant overhearing, periodic beaconing for Neighbourhood Management (NM) and increase in packet header length to append priority wise sorted Forwarding Candidates Set (FCS) prior to data transmission. The timer-based coordination mechanism incurs the least overhead to coordinate among the FCS that has successfully received the data packet for relaying the data in a multi-hop manner. This timer-based mechanism suffers from duplicate transmissions if the FCS is either not carefully selected or coordinated. The focus of this work is to propose a hybrid opportunistic energy efficient routing design for large scale, low power and lossy WSN. This design avoids periodic 'hello' beacons for NM, limits constant overhearing and increase in packet header length. There are two modes of operation i) opportunistic ii) unicast mode. The sender node adopts opportunistic forwarding for its initial data packet transmission and instead of pre-computing the FCS, it is dynamically computed in a completely distributed manner. The eligible nodes to be part of FCS will be neighbour nodes at lower corona level than the sender with respect to the sink and remaining energy above the minimum threshold. The nodes part of FCS based on cross-layered multi-metrics and fuzzy decision logic determines its priority level to compute Dynamic Holding Delay (DHD) for effective timer coordination. The differentiated back off implementation along with DHD enables the higher priority candidate that had received data packet to forward the packet first and facilitates others to cancel its timer upon overhearing. The sender node switches to unicast mode of forwarding for successive transmissions by choosing the forwarding node with maximum trust value as it denotes the stability of the temporally varying link with respect to the forwarder. The sender node will revert to opportunistic mode to increase transmission reliability in case of link-level transmission error or no trustworthy forwarders. Simulation results in NS2 show significant increase in Packet Delivery Ratio (PDR),decrease in both average energy consumption per node and Normalized Energy Consumption (NEC) per packet in comparison with existing protocols.
Routing, Opportunistic, Energy Efficiency, fuzzy logic, scalability, communication protocol design
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A LOW-ENERGY DATA AGGREGATION PROTOCOL USING AN EMERGENCY EFFICIENT HYBRID MEDIUM ACCESS CONTROL PROTOCOL IN HIERARCHAL WIRELESS SENSOR NETWORKS
Rana Adel1 and Tamer Barakat2 , 1Faculty of engineering, Egypt , 2Faculty of engineering, Egypt
Recent wireless sensor network focused on developing communication networks with minimal power and cost. To achieve this, several techniques have been developed to monitor a completely wireless sensor network. Generally, in the WSN network, communication is established between the source nodes and the destination node with an abundant number of hops, an activity which consumes much energy. The node existing between source and destination nodes consumes energy for transmission of data and maximize network lifetime. To overcome this issue, a new Emergency Efficient Hybrid Medium Access Control (EEHMAC) protocol is presented to reduce consumption of energy among a specific group of WSNs which will increase the network lifetime. The proposed model makes a residual battery is utilized for effective transmission of data with minimal power consumption. Compared with other models, the experimental results strongly showed that our model is not only able to reduce network lifetime but also to increase the overall network performance.
Wireless sensor networks, data aggregation, EEHMAC protocol and clustering in WSN
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A NOVEL SECURITY PROTOCOL FOR WIRELESS SENSOR NETWORKS BASED ON ELLIPTIC CURVE SIGNCRYPTION
Anuj Kumar Singh1 and B.D.K.Patro2 , 1Dr. A.P.J. Abdul Kalam Technical University, Lucknow , 2Rajkiya Engineering College, Kannauj, India
With the growing usage of wireless sensors in a variety of applications including Internet of Things, the security aspects of wireless sensor networks have been on priority for the researchers. Due to the constraints of resources in wireless sensor networks, it has been always a challenge to design efficient security protocols for wireless sensor networks. An novel elliptic curve signcryption based security protocol for wireless sensor networks has been presented in this paper, which provides anonymity, confidentiality, mutual authentication, forward security, secure key establishment, and key privacy at the same time providing resistance from replay attack, impersonation attack, insider attack, offline dictionary attack, and stolen-verifier attack. Results have revealed that the proposed elliptic curve signcryption based protocol consumes the least time in comparison to other protocols while providing the highest level of security.
Wireless Sensor Network, Security, Protocol, Signcryption, Elliptic Curve
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AN ENHANCEMENT OF CLUSTER-BASED FALSE DATA FILTERING SCHEME THROUGH DYNAMIC SECURITY SELECTION IN WIRELESS SENSOR NETWORKS
Jungsub Ahn and Taeho Cho , Sungkyunkwan University, Korea
Today, wireless sensor networks (WSNs) are applied to various industries such as building automation, medical, security, intelligent agriculture, and disaster monitoring. A WSN consists of hundreds to thousands of tiny sensor nodes that perform monitoring tasks. A small sensor node has a limited amount of internal memory and energy resources. Sensor nodes are used to detect a variety of data in specific environmental areas. As a result, WSN should be energy efficient. Sensor nodes are vulnerable to false report injection attacks because they are deployed in an open environment. A false report injection attack consumes the limited energy of a node more quickly and confuses the user. CFFS has been proposed to prevent such an attack using a method of en- route filtering false reports by dividing nodes into clusters. However, the CFFS scheme is vulnerable for repeated false report injection attacks. In this paper, we propose an approach to prolong the WSN lifetime by adjusting the dynamic security threshold value and using a fuzzy logic-based key redistribution selection of cluster head nodes. The proposed method increases the detection rate for repeated false report injection attacks by adding the additional key distribution phase in the existing method. The experimental results show that the energy efficiency of the proposed method was increased by 40.278%.
False Report Injection Attack, Cluster-based False Data Filtering, Network Lifetime Extension, Fuzzy-Logic System
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MULTI-CLUSTER MULTI-CHANNEL SCHEDULING (MMS) ALGORITHM FOR MAXIMUM DATA COLLECTION WITH DELAY MINIMIZATION IN WSN
A. Vijayalakshmi1* and P. Vanaja Ranjan2 , 1* Vels Institute of Science, India , 2 Anna University, India
Interference during data transmission can cause performance degradation like packet collisions in Wireless Sensor Networks (WSNs). While multi-channels available in IEEE 802.15.4 protocol standard WSN technology can be exploited to reduce interference, allocating channel and channel switching algorithms can have a major impact on the performance of multi-channel communication. This paper presents an improved Fuzzy Logic based Cluster Formation and Cluster Head (CH) Selection algorithm with enhanced network lifetime for multi-cluster topology. The Multi-Cluster Multi-Channel Scheduling (MMS) algorithm proposed in this paper improves the data collection by minimizing the maximum interference and collision. The presented work has developed Cluster formation and cluster head (CH) selection algorithm and Interference-free data communication by proper channel scheduled. The extensive simulation and experimental outcomes prove that the proposed algorithm not only provides an interference-free transmission but also provides delay minimization and longevity of the network lifetime, which makes the presented algorithm suitable for energy-constrained wireless sensor networks.
Wireless Sensor Networks, Fuzzy Logic, Cluster Formation, Cluster Head, Channel Assignment, Channel Switching, Delay Minimization, Network Lifetime
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AN OPTIMUM ENERGY CONSUMPTION HYBRID ALGORITHM FOR XLN STRATEGIC DESIGN IN WSN’S
Md. Khaja Mohiddin1 and V. B. S. Srilatha Indira Dutt2 , 1 GITAM (Deemed to be University), India , 2 GITAM (Deemed to be University), India
In this paper, X-Layer protocol is originated which executes mobility error prediction (MEP) algorithm to calculate the remaining energy level of each node. This X-Layer protocol structure employs the mobility aware protocol that senses the mobility concerned to each node with the utilization of Ad-hoc On-Demand Distance Vector (AODV), which shares the information or data specific to the distance among individual nodes. With the help of this theory, the neighbour list will be updated only to those nodes which are mobile resulting in less energy consumption when compared to all (static/mobile) other nodes in the network. Apart from the MEP algorithm, clustering head (CH) election algorithm has also been specified to identify the relevant clusters whether they exists within the network region or not. Also clustering multi-hop routing (CMHR) algorithm was implemented in which the node can identify the cluster to which it belongs depending upon the distance from each cluster surrounding the node. Finally comprising the AODV routing protocol with the Two-Ray Ground method, we implement X-Layer protocol structure by considering MAC protocol in accordance to IEEE 802.15.4 to obtain the best results in energy consumption and also by reducing the energy wastage with respect to each node. The effective results had been illustrated through Network Simulator-II platform.
IEEE 802.15.4, AODV Protocol, Two Ray Ground Propagation Model, Mobility Error Prediction (MEP)Algorithm, Clustering Multi-Hop Routing (CMHR) Algorithm, Energy Consumption, End-to-End Delay; Throughput
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FUZZY GENE OPTIMIZED REWEIGHT BOOSTING CLASSIFICATION FOR ENERGY EFFICIENT DATA GATHERING IN WSN
J.Srimathi1 and V.Valli Mayil2 , 1 Bharathiar University, India , 2 Periyar Maniammai University, India
The energy is a major resource to obtain efficient data gathering and increasing network lifetime (NL). The various techniques are introduced for data aggregation, but energy optimized sensor node (SN) selection was not carried out to further enhance NL. In order to improve the energy efficient data gathering in WSN, a Fuzzy Gene Energy Optimized Reweight Boosting Classification (FGEORBC) Technique is introduced with lesser time consumption. In FGEORBC technique, the Residual Energy (RE) of SN in the WSN is computed. After calculating SN residual energy, fuzzy logic is applied to determine higher energy nodes and lower energy nodes using threshold value. For finding the optimal higher energy SNs, then Ranked Gaussian gene optimization technique is applied. If the node satisfies the fitness criterion, then the node is selected as an optimal higher energy SN. Otherwise, the rank selection, ring crossover, and Gaussian mutation process are carried out until the condition gets satisfied. After that, the sink node collects the data packets (DP) from the optimal higher energy SNs. In the sink node, Reweight Boosting Classification is carried out to classify the sensed DP and it sends to the base station (BS) for further processing. Simulation of FGEORBC technique is carried out using different parameters such as energy consumption (EC), NL, data gathering time and classification accuracy (CA) with respect to a number of SN and a number of DP. The results observed that FGEORBC technique improves the data gathering and NL with minimum time as well as EC than the state-of-the-art methods.
WSN, data gathering, residual energy, fuzzy logic, Ranked Gaussian gene optimization, data classification, Reweight Boosting Classification
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