Sukhchandan Randhawa

Data Aggregation in Wireless Sensor Networks: Previous Research, Current Status and Future Directions

Wireless sensor networks (WSNs) consist of large number of small sized sensor nodes, whose main task is to sense the desired phenomena in a particular region of interest. These networks have large number of applications such as habitat monitoring, disaster management, security and military etc. Sensor nodes are very small in size and have limited processing capability as these nodes have very low battery power. WSNs are also prone to failure, due to low battery power constraint. Data aggregation is an energy efficient technique in WSNs. Due to high node density in sensor networks same data is sensed by many nodes, which results in redundancy. This redundancy can be eliminated by using data aggregation approach while routing packets from source nodes to base station. Researchers still face trouble to select an efficient and appropriate data aggregation technique from the existing literature of WSNs. This research work depicts a broad methodical literature analysis of data aggregation in the area of WSNs in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 123 research papers out of large collection of 932 research papers published in 20 foremost workshops, symposiums, conferences and 17 prominent journals. The current status of data aggregation in WSNs is distributed into various categories. Methodical analysis of data aggregation in WSNs is presented which includes techniques, tools, methodology and challenges in data aggregation. The literature covered fifteen types of data aggregation techniques in WSNs. Detailed analysis of this research work will help researchers to find the important characteristics of data aggregation techniques and will also help to select the most suitable technique for data aggregation. Research issues and future research directions have also been suggested in this research literature.

Energy-efficient load balancing scheme for two-tier communication in wireless sensor networks

Wireless sensor network (WSN) has gained an enormous attention of researchers with its dynamic applications. Energy is considered as a scarce and the most vital resource in WSNs. In clustering-based approach, there is huge energy consumption while communicating data from cluster to base station (BS) and from sensor nodes to cluster head within a cluster. The repetitive use of same nodes and paths can result in network hole problem and service unavailability. There are number of research areas which have addressed the issues of energy efficiency and service availability. Load balancing is considered as one of the key techniques which are used to balance the trade-off between energy efficiency and service availability. In this research work, a novel real-time energy efficient load balancing technique for two-tier communication is proposed in which, initially in Tier 1, the energy consumption is reduced for communication between cluster to BS by applying space-time block coding over M-ary quadrature amplitude modulation and binary phase-shift keying modulations. In Tier 2, within a cluster, the energy consumption of communication among sensor nodes and CHs is reduced by utilizing the concepts of feedback control system, in which there is no need of the knowledge of static traffic demands. The performance of the proposed technique has been compared with the existing techniques in terms of energy utilization variation with varying order of transmit diversity and varying cluster BS distances, optimization of constellation sizes, energy utilization variation with cluster–BS distances for varying maximum link utilization and data rates along with varying traffic distribution and topology.

MLBC: Multi-objective Load Balancing Clustering technique in Wireless Sensor Networks

Abstract In Wireless Sensor Networks (WSN) there are scenarios with conflicting objectives. This needs to be modeled as multi-objective optimization formulation. The optimization problem changes with the nature of application, network scenario and input/output parameters. Depending upon the underlying application requirements, there is a need of an appropriate Multi-Objective Optimization technique to manage these multiple conflicting objectives simultaneously and to yield an overall optimal solution. In this paper, a novel Multi-objective Load Balancing Clustering (MLBC) technique is proposed by utilizing Multi Objective Particle Swarm Optimization (MOPSO). Two objective functions are defined: Energy Efficiency and Reliability. The energy efficiency is based on the average residual energy of Cluster Heads (CHs), whereas the reliability is based on the communication cost of inter-cluster routing. The nodes transmit their information to Cluster Head or Base Station in single-hop or multi-hop manner. A healing function is utilized to avoid loops in the generated path. The load balancing is performed by shuffling the roles of next hop node and Cluster Head in each iteration. The best compromise solution is selected through the fuzzy decision-based approach. The performance of the proposed and existing approaches has been evaluated in terms of energy efficiency, network lifetime, packet delivery ratio, data accuracy and number of active nodes. Apart from these parameters, coverage and scalability are also considered to evaluate the quality of solutions provided by multi-objective optimization approach.

Multi-objective Data Aggregation for Clustered Wireless Sensor Networks

Maximizing the energy efficiency is one of the major challenges in Wireless Sensor Networks. Research works have shown that by cluster formation of nodes, energy can be more efficiently used. In this research work, a Multi-objective Data Aggregation Clustering (MDAC) technique is proposed based on multi-objective optimization approach. Non-dominated Sorting Genetic Algorithm-II is utilized for cluster formation which can consider the several objective functions defined simultaneously. The main objectives are to minimize the communication cost among cluster heads, base station and cluster members and also to maximize the number of nodes within a cluster. The selection of CH nearer to BS is also avoided in order to prevent the hot spot problem. NSGA-II presents different solutions in a solution set which result in different topologies. Every solution in a solution set represents the best solution based on objective functions. BS considers every solution instance in solution set and selects the most suitable solution based on the desired criteria. The experimental evaluation results show that the proposed MDAC technique performs better than existing multi-objective clustering techniques in terms of throughput, total energy consumption, network lifetime, number of active nodes, data received at BS and variation in network lifetime and energy with varying selection choices of NSGA-II algorithm.

DAHDA: Dynamic Adaptive Hierarchical Data Aggregation for Clustered Wireless Sensor Networks

A Wireless Sensor Network (WSN) has gained a tremendous attention of researchers with its dynamic applications. The constant monitoring of critical scenarios has make WSNs an attractive choice for researchers at a large scale. The main objective is to increase the network lifetime for optimal and efficient utilization of resources in WSNs. For optimum functionality, various approaches have been proposed based upon clustering. Network lifetime is related with energy level of sensor nodes deployed in region of interest. As sensor nodes have limited lifetime, so there is a need to develop an algorithm for aggregating the sensors data in WSNs. A novel Dynamic Adaptive Hierarchical Data Aggregation (DAHDA) algorithm has been presented for evolving, uniform and non-uniform networks while maintaining the data accuracy. In addition, the algorithm is able to handle sudden bursts in the underlying data by recording the data in the area of interest for the whole event duration. The experimental evaluation on real and synthetic data shows that the algorithm performs well in terms of extending the lifetime of the network, maintaining the original distribution of the sensors as long as possible and maintaining the accuracy of the sensed data. DAHDA is an adaptive hierarchical aggregation algorithm for WSNs. The proposed algorithm has been simulated and its performance has been compared with the existing approaches in terms of residual energy, number of alive nodes, data accuracy, sudden burst detection, sensor distribution, lifetime of last node, first node and average lifetime of node for uniform, non-uniform and evolving networks.

Resource scheduling in cloud using harmony search

Cloud computing is an emerging technology and is getting popular because of its advantages. Cloud computing is a computing paradigm used to provide software, infrastructure and platform as a service to users via the internet. Its a challenging problem to manage cloud computing resources efficiently. The objective is to allocate resources to users requests such that user requests are completed in minimum time and resources are used efficiently. To solve the cloud scheduling problem, different algorithms are proposed. Based on Harmony Search, a new metaheuristic scheduling approach is discussed in this paper and its performance is evaluated against the sample data.

An Efficient Trust Management Algorithm in Wireless Sensor Network

The wireless sensor network (WSN) is a widely distributed and powerful technology which is used in various fields but due to restrictions in constraints is more vulnerable to attack. To mitigate from these attacks a technique called “trust” is used. We propose a simple and efficient algorithm that calculates the value of the trusted node and finds the malicious node. An improved algorithm is proposed that selects the nearby cluster head on the basis of the received signal and calculates the trust value on the basis of the packet forwarding factor. In this paper we analyze the consistency of clusters and lifetime of the network.