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Due to enormous growth of Internet of Things (IoT) in the last decade, the amount of data generated through smart devices is increasing exponentially. Fog computing has emerged as a potential technology to deal such a huge volume of data in which task offloading is the most important aspect which has attracted significant attention. Many research works have been carried out, however, task offloading with latency sensitivity, reliability and result migration over a mobile user environment is still not widely addressed. In this paper, we propose a method for delay-sensitive and fault minimized task offloading for service requests made through a mobile/vehicular end user environment implemented via Software Defined Network (SDN) controllers integrated with the fog layer. This is a novel multi-phased model involving determining the optimal number of SDN controllers, clustering of the fog nodes (FNs) on the basis of SDN proximities, task prioritization and Gravitational Search Algorithm (GSA) based target FN selection. The simulation outcomes of our proposed approach show that there is a reduction in delay by around 23%–30% and around 60%–80% lesser number of tasks unassigned in each round as compared to two base algorithms. [ABSTRACT FROM AUTHOR]

The main constraint of wireless sensor networks (WSNs) is the limited and generally irreplaceable power source of the sensor nodes. Therefore, designing energy saving routing algorithm is one of the most focused research issues. In this paper, we propose an energy aware routing algorithm for cluster based WSNs. The algorithm is based on a clever strategy of cluster head (CH) selection, residual energy of the CHs and the intra-cluster distance for cluster formation. To facilitate data routing, a directed virtual backbone of CHs is constructed which is rooted at the sink. The proposed algorithm is also shown to balance energy consumption of the CHs during data routing process. We prove that the algorithm achieves constant message and linear time complexity. We test the proposed algorithm extensively. The experimental results show that the algorithm outperforms other existing algorithms in terms of network lifetime, energy consumption and other parameters. [ABSTRACT FROM AUTHOR]

Clustering is an efficient topology control method which balances the traffic load of the sensor nodes and improves the overall scalability and the life time of the wireless sensor networks (WSNs). However, in a cluster based WSN, the cluster heads (CHs) consume more energy due to extra work load of receiving the sensed data, data aggregation and transmission of aggregated data to the base station. Moreover, improper formation of clusters can make some CHs overloaded with high number of sensor nodes. This overload may lead to quick death of the CHs and thus partitions the network and thereby degrade the overall performance of the WSN. It is worthwhile to note that the computational complexity of finding optimum cluster for a large scale WSN is very high by a brute force approach. In this paper, we propose a novel differential evolution (DE) based clustering algorithm for WSNs to prolong lifetime of the network by preventing faster death of the highly loaded CHs. We incorporate a local improvement phase to the traditional DE for faster convergence and better performance of our proposed algorithm. We perform extensive simulation of the proposed algorithm. The experimental results demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Energy efficient clustering and routing are two well known optimization problems which have been studied widely to extend lifetime of wireless sensor networks (WSNs). This paper presents Linear/Nonlinear Programming (LP/NLP) formulations of these problems followed by two proposed algorithms for the same based on particle swarm optimization (PSO). The routing algorithm is developed with an efficient particle encoding scheme and multi-objective fitness function. The clustering algorithm is presented by considering energy conservation of the nodes through load balancing. The proposed algorithms are experimented extensively and the results are compared with the existing algorithms to demonstrate their superiority in terms of network life, energy consumption, dead sensor nodes and delivery of total data packets to the base station. [Copyright &y& Elsevier]

Abstract: Clustering sensor nodes is an effective topology control method to reduce energy consumption of the sensor nodes for maximizing lifetime of Wireless Sensor Networks (WSNs). However, in a cluster based WSN, the leaders (cluster heads) bear some extra load for various activities such as data collection, data aggregation and communication of the aggregated data to the base station. Therefore, balancing the load of the cluster heads is a challenging issue for the long run operation of the WSNs. Load balanced clustering is known to be an NP-hard problem for a WSN with unequal load of the sensor nodes. Genetic Algorithm (GA) is one of the most popular evolutionary approach that can be applied for finding the fast and efficient solution of such problem. In this paper, we propose a novel GA based load balanced clustering algorithm for WSN. The proposed algorithm is shown to perform well for both equal as well as unequal load of the sensor nodes. We perform extensive simulation of the proposed method and compare the results with some evolutionary based approaches and other related clustering algorithms. The results demonstrate that the proposed algorithm performs better than all such algorithms in terms of various performance metrics such as load balancing, execution time, energy consumption, number of active sensor nodes, number of active cluster heads and the rate of convergence. [Copyright &y& Elsevier]

Abstract: Clustering is an efficient technique to improve scalability and life time of a wireless sensor network. In this paper, we present an Energy Efficient Load-Balanced Clustering (EELBC) Algorithm that addresses energy efficiency as well as load balancing. EELBC is a min-heap based clustering algorithm. A min-heap is build using cluster heads (CHs) on the number of sensor nodes allotted to the CHs. We show that the algorithm runs in O (n log m) time for n sensor nodes and m CHs. The experimental results show the efficiency of the proposed algorithm in terms of load balancing, energy efficiency, execution time and also the number of sensor nodes die during the network period. [Copyright &y& Elsevier]

Abstract: In this paper, we propose, a novel DBSCAN method to cluster the gene expression data. The main problem of DBSCAN is its quadratic computational complexity. We resolve this drawback by using the prototypes produced from a squared error clustering method such as K-means. Then, the DBSCAN technique is applied efficiently using these prototypes. In our algorithm, during the iterations of DBSCAN, if a point from an uncovered prototype is assigned to a cluster, then all the other points of such prototype belongs to the same cluster. We have carried out excessive experiments on various two dimensional artificial and multi-dimensional biological data. The proposed technique is compared with few existing techniques. It is observed that proposed algorithm outperforms the existing methods. [Copyright &y& Elsevier]

Abstract: K-Means algorithm is one of the famous partitioning clustering techniques that has been studied extensively. However, the major problem with this method that it cannot ensure the global optimum results due to the random selection of initial cluster centers. In this paper, we present a novel method that selects the initial cluster centers with the help of Voronoi diagram constructed from the given set of data points. The initial cluster centers are effectively selected from those points which lie on the boundary of higher radius Voronoi circles. As a result, the proposed method automates the selection of the initial cluster centers to supply them for K-means. The proposed method is experimented on various artificial (hand-made) as well as real world data sets of various dimensions. It is observed that it is able to produce better clustering results than the traditional K-means and the improved K-means. [Copyright &y& Elsevier]

Conservation of energy and fault tolerance are two major issues in the deployment of a wireless sensor network (WSN). Design of clustering and routing algorithms for a large scale WSN should incorporate both these issues for the long run operation of the network. In this paper, we propose distributed clustering and routing algorithms jointly referred as DFCR. The algorithm is shown to be energy efficient and fault tolerant. The DFCR uses a distributed run time recovery of the sensor nodes due to sudden failure of the cluster heads (CHs). It takes care of the sensor nodes which have no CH within their communication range. We perform extensive experiments on the proposed algorithm using various network scenarios. The experimental results are compared with the existing algorithms to demonstrate the strength of the algorithm in terms of various performance metrics. [ABSTRACT FROM AUTHOR]