Distributed Energy Efficient Clustering Routing Protocol for Wireless Sensor Networks Using Affinity Propagation And Fuzzy Logic

Organizing nodes into clusters and forwarding data to the Base Station (BS) in clustering routing protocols have been widely utilized to improve the energy efficiency, scalability and stability of Wireless Sensor Networks (WSN). Making decisions on how many clusters are formed, which nodes are selected as Cluster Heads (CHs) and who become the relay nodes significantly impact the network performance. Therefore, a Distributed clustering routing protocol combined Affinity Propagation (AP) with Fuzzy Logic called DAPFL is proposed in this paper, which considers not only energy efficiency but also energy balance to extend the network lifetime. In DAPFL, AP is firstly used to determine the number of clusters and select the best CHs simultaneously based on residual energy, distance between nodes. Then the optimal next-hop CHs are chosen by using fuzzy logic system with residual energy, data length and distance to BS as descriptors. Simulations in different scenarios are carried out to verify the effectiveness of DAPFL, and the results show that DAPFL exhibits the promising performance in terms of network energy consumption, standard deviation of residual energy, network throughput and lifetime, compared with the up-to-date distributed clustering routing protocols EEFUC, EEFRP, LEACH-AP and APSA.

A Survey of Data Mining Activities in Distributed Systems

Distributed systems, which may be utilized to do computations, are being developed as a result of the fast growth of sharing resources. Data mining, which has a huge range of real applications, provides significant techniques for extracting meaningful and usable information from massive amounts of data. Traditional data mining methods, on the other hand, suppose that the data is gathered centrally, stored in memory, and is static. Managing massive amounts of data and processing them with limited resources is difficult. Large volumes of data, for instance, are swiftly generated and stored in many locations. This becomes increasingly costly to centralize them at a single location. Furthermore, traditional data mining methods typically have several issues and limitations, such as memory restrictions, limited processing ability, and insufficient hard drive space, among others. To overcome the following issues, distributed data mining's have emerged as a beneficial option in several applications According to several authors, this research provides a study of state-of-the-art distributed data mining methods, such as distributed common item-set mining, distributed frequent sequence mining, technical difficulties with distributed systems, distributed clustering, as well as privacy-protection distributed data mining. Furthermore, each work is evaluated and compared to the others.

Research on Parallel Distributed Clustering Algorithm Applied to Milling Parameter Optimization

In the big data era, traditional data mining technology cannot meet the requirements of massive data processing with the background of intelligent manufacturing. Aiming at insufficient computing power and low efficiency in mining process, this paper proposes a improved K-means clustering algorithm based on the concept of distributed clustering in cloud computing environment. The improved algorithm (T.K-means) is combined with MapReduce computing framework of Hadoop platform to realize parallel computing, so as to perform processing tasks of massive data. In order to verify the practical performance of T.K-means algorithm, taking machining data of milling Ti-6Al-4V alloy as the mining object. The mapping relationship among milling parameters, surface roughness and material removal rate is mined, and the optimized value for milling parameters are obtained. The results show that T.K-means algorithm can be used to mine the optimal milling parameters, so that the best surface roughness can be obtained in milling Ti-6Al-4V titanium alloy.

A Robust Distributed Clustering of Large Data Sets on a Grid of Commodity Machines

Distributed clustering algorithms have proven to be effective in dramatically reducing execution time. However, distributed environments are characterized by a high rate of failure. Nodes can easily become unreachable. Furthermore, it is not guaranteed that messages are delivered to their destination. As a result, fault tolerance mechanisms are of paramount importance to achieve resiliency and guarantee continuous progress. In this paper, a fault-tolerant distributed k-means algorithm is proposed on a grid of commodity machines. Machines in such an environment are connected in a peer-to-peer fashion and managed by a gossip protocol with the actor model used as the concurrency model. The fact that no synchronization is needed makes it a good fit for parallel processing. Using the passive replication technique for the leader node and the active replication technique for the workers, the system exhibited robustness against failures. The results showed that the distributed k-means algorithm with no fault-tolerant mechanisms achieved up to a 34% improvement over the Hadoop-based k-means algorithm, while the robust one achieved up to a 12% improvement. The experiments also showed that the overhead, using such techniques, was negligible. Moreover, the results indicated that losing up to 10% of the messages had no real impact on the overall performance.

A Hybrid Efficient Distributed Clustering Algorithm Based Intrusion Detection System to Enhance Security in MANET

MANET plays a key role in the contemporary improvements in technology and services that dynamically builds up the network connection bringing about a variation in network topology. If any malevolent activity inside the network otherwise in the system occurs it is monitored by software application or device called Intrusion or Invader Detection System (IDS). The power utilization is more in MANET as the IDS needs to be active for the whole time on each node. Considering this, we have designed a clustering-based proposal for Ad-hoc networks. To lower the communication overhead, the CH detection is called upon for establishing the path Weighted Clustering Algorithm (DCA) that is utilized to construct path. The proposed Distributed Clustering Algorithm dependent Invader Detection System (DCAIDS) is designed such that it reduces the overall delay during transmission in a network. The intruder attacks are identified by IDS and that particular nodes are detached from the cluster. When the particular target node receives the packets, it acknowledges its reception by sending RREP message so that the sender can send the intended data packets thereafter. Thus, an efficient and delay tolerant path is established which in turn intensifies the certainty of MANET.