Study on Different Topology Manipulation Algorithms in Wireless Sensor Network

Wireless sensor network (WSN) comprises of spatially distributed autonomous sensors to screen physical or environmental conditions and to agreeably go their information through the network to a principle area. One of the critical necessities of a WSN is the efficiency of vitality, which expands the life time of the network. At the same time there are some different variables like Load Balancing, congestion control, coverage, Energy Efficiency, mobility and so on. A few methods have been proposed via scientists to accomplish these objectives that can help in giving a decent topology control. In the piece, a few systems which are accessible by utilizing improvement and transformative strategies that give a multi target arrangement are examined. In this paper, we compare different algorithms' execution in view of a few parameters intended for every target and the outcomes are analyzed. DOI: 10.17762/ijritcc2321-8169.15029

Genetic-fuzzy based load balanced protocol for WSNs

Recent advancement in wireless sensor networks primarily depends upon energy constraint. Clustering is the most effective energy-efficient technique to provide robust, fault-tolerant and also enhance network lifetime and coverage. Selection of optimal number of cluster heads and balancing the load of cluster heads are most challenging issues. Evolutionary based approach and soft computing approach are best suitable for counter the above problems rather than mathematical approach. In this paper we propose hybrid technique where Genetic algorithm is used for the selection of optimal number of cluster heads and their fitness value of chromosome to give optimal number of cluster head and minimizing the energy consumption is provided with the help of fuzzy logic approach. Finally cluster heads uses multi-hop routing based on A*(A-star) algorithm to send aggregated data to base station which additionally balance the load. Comparative study among LEACH, CHEF, LEACH-ERE, GAEEP shows that our proposed algorithm outperform in the area of total energy consumption with various rounds and network lifetime, number of node alive versus rounds and packet delivery or packet drop ratio over the rounds, also able to balances the load at cluster head

An energy-efficient routing protocol for Hybrid-RFID Sensor Network

Radio Frequency Identification (RFID) systems facilitate detection and identification of objects that are not easily detectable or distinguishable. However, they do not provide information about the condition of the objects they detect. Wireless sensor networks (WSNs), on the other hand provide information about the condition of the objects as well as the environment. The integration of these two technologies results in a new type of smart network where RFID-based components are combined with sensors. This research proposes an integration technique that combines conventional wireless sensor nodes, sensor-tags, hybrid RFID-sensor nodes and a base station into a smart network named Hybrid RFID-Sensor Network (HRSN)

TECA : A Topology and Energy Control Algorithm for Sensor Networks

A main challenge in the field of sensor networks is energy efficiency to prolong the sensor's operational lifetime. Due to low-cost hardware, nodes' placement or hardware design, recharging might be impossible. Since most energy is spent for radio communication, many approaches exist that put sensor nodes into sleep mode with the communication radio turned off. In this paper, we propose a new Topology and Energy Control Algorithm called TECA. We will show the performance of TECA by means of extensive simulations compared to two other approaches. In terms of operational lifetime, packet delivery and network connectivity, TECA shows promising results. Unlike many other simulations, we use an appropriate link loss model that was verified in reality. By measuring packet delivery rates, TECA is able to adapt to different environments while still maintaining network connectivity

Empirical analysis of dynamic load balancing techniques in cloud computing

Virtualization, dispersed registration, systems administration, programming, and web administrations are all examples of distributed computing. Customers, datacenters, and scattered servers are just a few of the components that make up a cloud. It includes things like internal failure adaption, high accessibility, flexibility, adaptability, lower client overhead, lower ownership costs, on-demand advantages, and so on. The basis of a feasible load adjusting computation is key to resolving these challenges. CPU load, memory limit, deferral, and system load are all examples of heaps. Burden adjustment is a method for distributing the load across the many hubs of a conveyance framework in order to optimize asset utilization and employment response time while avoiding a situation where some hubs are heavily loaded while others are idle or performing little work. Burden adjustment ensures that at any one time, each processor in the framework or each hub in the system does about the same amount of work. This method may be initiated by the sender, the collector, or the symmetric sort (the blend of sender-started and recipient started types). With some example data center loads, the goal is to create several dynamic load balancing techniques such as Round Robin, Throttled, Equally Spread Current Execution Load, and Shortest Job First algorithms

Joint Head Selection and Airtime Allocation for Data Dissemination in Mobile Social Networks

Mobile social networks (MSNs) enable people with similar interests to interact without Internet access. By forming a temporary group, users can disseminate their data to other interested users in proximity with short-range communication technologies. However, due to user mobility, airtime available for users in the same group to disseminate data is limited. In addition, for practical consideration, a star network topology among users in the group is expected. For the former, unfair airtime allocation among the users will undermine their willingness to participate in MSNs. For the latter, a group head is required to connect other users. These two problems have to be properly addressed to enable real implementation and adoption of MSNs. To this aim, we propose a Nash bargaining-based joint head selection and airtime allocation scheme for data dissemination within the group. Specifically, the bargaining game of joint head selection and airtime allocation is first formulated. Then, Nash bargaining solution (NBS) based optimization problems are proposed for a homogeneous case and a more general heterogeneous case. For both cases, the existence of solution to the optimization problem is proved, which guarantees Pareto optimality and proportional fairness. Next, an algorithm, allowing distributed implementation, for join head selection and airtime allocation is introduced. Finally, numerical results are presented to evaluate the performance, validate intuitions and derive insights of the proposed scheme

Optimization and Learning in Energy Efficient Cognitive Radio System

Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network

A distributed algorithm for semantic collectors election in wireless sensors networks

Semantic clustering is a recent technique for saving energy in wireless sensor networks. Its mechanism of action consists in dividing the network into groups (clusters) formed by semantically related nodes and at least one semantic collector, which acts as a bridge between its internal nodes and the sink node. Since semantic collector nodes need to perform more tasks than normal nodes, they deplete their energy budget faster, so it is necessary to use efficient mechanisms for electing semantic collectors to prolong the network lifetime. Our hypothesis is that an effective choice of semantic collectors allows a longer network lifetime. To test it, we start from a previous work of the authors of this article and we propose an algorithm for electing semantic collectors in a distributed way based on a fuzzy inference engine. The inputs of the inference engine are the residual energy of nodes and their received signal strength indicator (RSSI). Simulation results confirm our hypothesis, since the algorithm provides (i) an improvement of 17.4% in relation to another proposal of the related literature, and (ii) a gain of 68.8% over the time life of the network’s original work.Keywords: Wireless Sensors Networks, Semantic Cluster, Semantic Collector Election