Cyber–physical systems: Extending pervasive sensing from control theory to the Internet of Things

Wireless sensor network (WSN) comprises of huge amount of sensing nodes being used for collecting the data in different situations. WSN finds applications mostly to gather information from remote places such as in environment monitoring, military, in transportation security and so on. The main problem in WSN is the availability of limited energy resources. For enhancing energy efficiency with the lifespan of sensor nodes, in this research, an energy effective routing protocol, low energy adaptive clustering hierarchy (LEACH) along with optimization algorithm genetic algorithm (GA) has been presented. LEACH is a hierarchical protocol which converts the sensor nodes into cluster heads (CH), and CH gathers and compress the data and send it to the target node. Genetic algorithm helps to find the optimal route by using its fitness function. After simulating the code in MATLAB, the energy consumption rate up to 17.39% has been reduced when GA is used. At last, comparison between proposed work and existing work is performed to determine the efficiency of the proposed work.

Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks

System Reliability Theory: Models, Statistical Methods, and Applications (2nd ed.) (Book)

Many schemes have been proposed for energy-efficient routing in wireless sensor networks (WSNs). However, most of these algorithms focus only on energy efficiency in which each node finds a shortest path to the base station (BS), but remain silent about energy balancing which is equally important to prolong the network lifetime. In this paper, we propose particle swarm optimization-based routing and clustering algorithms for WSNs. The routing algorithm builds a trade-off between energy efficiency and energy balancing, whereas the clustering algorithm takes care of the energy consumption of gateways as well as sensor nodes. We develop an efficient particle-encoding scheme and derive a multi-objective fitness function for each of the proposed routing and clustering algorithms. The algorithms are also capable of tolerating the failure of cluster heads. We perform extensive simulations on the proposed schemes and the results are compared with the existing algorithms to demonstrate their superiority in terms of various performance metrics.

PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks

Particle swarm optimization for maximizing lifetime of wireless sensor networks

Energy conservation and fault tolerance are the most two important challenging issues for the development of large scale wireless sensor networks (WSNs). Failure of cluster heads in a cluster based WSN is more catastrophic as they are responsible not only for data aggregation and transmission of the aggregated data to the base station but also relaying data for multi-hop communication. Therefore, a routing algorithm in WSNs should be energy aware as well as fault tolerant. In this paper, we present a distributed energy efficient and fault tolerant routing algorithm for WSNs. The algorithm selects next-hop cluster head in energy efficient manner in the data routing phase and carefully restores the connectivity of the neighbours of a cluster head in case of its failure. The algorithm is tested extensively by considering several scenarios of WSN. The experimental results are compared with the existing algorithms in terms of several metrics to show the effectiveness of the proposed algorithm.

A distributed algorithm for energy efficient and fault tolerant routing in wireless sensor networks

In multi-cloud environment, task scheduling has attracted a lot of attention due to NP-Complete nature of the problem. Moreover, it is very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. In this paper, we propose a genetic algorithm (GA) based approach for solving task scheduling problem. The algorithm is described with innovative idea of fitness function derivation and mutation. The proposed algorithm is exposed to rigorous testing using various benchmark datasets and its performance is evaluated in terms of total makespan.

A GA based approach for task scheduling in multi-cloud environment

Cloud computing is one of the fastest growing technologies which delivers online services to a consumer on pay-per-use basis. Recently, the concept of multi-cloud environment has been evolved in the recent years in which workloads are distributed among the data centers of multiple clouds. However, task scheduling in a multi-cloud environment is more challenging as the resources of the data centers belonged to the clouds are heterogeneous in nature. In this paper, we propose an efficient task scheduling algorithm for multi-cloud environment. We perform extensive simulation of the proposed algorithm on benchmark data and compare the results with the existing algorithms. We show that the algorithm performs better than the existing algorithms in terms of make span and resource utilization.

Md. Azharuddin

Papers

A distributed algorithm for energy efficient and fault tolerant routing in wireless sensor networks

A GA based approach for task scheduling in multi-cloud environment

An Effective Task Scheduling Approach for Cloud Computing Environment