Node Failure Management to Improve the Performance of Wireless Sensor Networks

A sensor network can be defined an assembly of sensor nodes which associated by all together to complete particular detailed task. These sensor nodes are mostly in huge amounts also compactly installed moreover in the network area or very near to it. Sensor networks can be worked for several sectors such that: environmental monitoring, home, health care, Industries, military, and habitat. Failure of network is unavoidable in wireless sensor networks because of unfriendly location and non-reachable placement. Hence, it is needed that network faults are discovered in time and proper methods are engaged to bear network task. So, it is important to deliver fault forbearing systems for spread sensor applications. Numerous new work in this field yield severely different methodologies to talking the fault tolerance concern in routing. In this propose review and equate present fault tolerant practices to provision for sensor applications.

Research on Distance Teaching System of English Course Based on Wireless Network Technology

The development in technology is taking place with an accelerating pace across the globe. The increasing expansion and advancement in the field of information technology and the modern teaching system provide a technical support for the development of a distance teaching system to learn English courses. Multimedia teaching system of English course based on B/S framework (system 1) and English teaching system based on MVC architecture (system 2) were the two most prominent and widely used approaches for the distance teaching system of English learning courses. These systems comprehensively consider the current English teaching needs, develop the existing architectures, discuss the system architecture and functions, and establish the corresponding development environment. However, the mentioned systems have the problem of high proportion of memory resource consumption and high failure rate of the communicating nodes. In order to reduce the proportion of memory resource consumption and node failure rate of distance teaching system and effectively improve the teaching effect, this study designed a distance teaching system of English course based on wireless network technology. In order to analyze the functionality and stability of the wireless network technology in distance teaching of English course, the server-side and client-side modules of the system are designed. The server side is mainly used to maintain and control the overall functions of the system, while the client side is used to access/request the contents from the server. On this basis, the system software module is designed. The memory consumption results are accounted for under 30%, which is significantly lower than the earlier-mentioned systems, and the node failure rate of the system proposed in this paper does not increase significantly and remains below 4% all the time which indeed is a very low amount of failure rate. The experimental results show that the memory resource consumption ratio and node failure rate of the proposed system are very low, and the application feedback effect is significantly better than the other systems.

An Overloading State Computation and Load Sharing Mechanism in Fog Computing

Fog computing is used to enrich the ability of cloud computing applications. Fog is a kind of buffer area placed between the data processing location and the data storage equipment in the network and plays a significant role in processing the real time data. The lack of resource provisioning approaches and high demand for IoT services make the fog node overloaded. Load balancing is a method to realize efficient resource utilization to avoid bottlenecks, overload, and fog node failure. This study suggests a concept to compute the probabilistic overloading state of a fog node and identification of fog node for load sharing. Each fog node computes Fstate and sends the message at regular intervals to the fog node coordinator (FNC). FNC maintains a fog that is utilized for offloading in case of fog overloading. A comparative study shows that the proposed model avoids an overloading state by the transfer of a certain number of requests to an underloaded fog node before actual overloading occurs. Numerical results validate theoretical investigation and efficiency of the proposed study.

Low-Power Failure Detection for Environmental Monitoring Based on IoT

Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms.

Head Node Selection Algorithm in Cloud Computing Data Center

Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, there are three modules, i.e., initial population generation, head node selection, and candidate node selection. In the first module, we generate the initial population by randomly mapping the task on different servers using a scheduling algorithm. After that, we compute the overall cost and the cost of each node based on resources. In the second module, the best optimal nodes are selected as a head node by applying the genetic operations such as crossover, mutation, and fitness function by considering the available resources. In the selected optimal nodes, one node is chosen as a head node and the other is considered as a candidate node. In the third module, the candidate node becomes the head node in the case of head node failure. The proposed method HNSA is compared against the state-of-the-art algorithms such as Bees Life Algorithm (BLA) and Heterogeneous Earliest Finished Time (HEFT). The simulation analysis shows that the proposed HNSA technique performs better in terms of execution time, memory utilization, service level sgreement (SLA) violation, and energy consumption.