Fuzzy Depth Based Routing Protocol for Underwater Acoustic Wireless Sensor Networks

Underwater Wireless Sensor Networks consist of a variable number of sensors and vehicles that are implemented to perform collaborative monitoring tasks over a given area. However, designing energy-efficient routing protocols for this type of networks is essential and challenging because the sensor nodes is powered by batteries, underwater environment is harsh and propagation delay is long. Most of the existing routing protocols used for underwater wireless sensor networks, such as depth based routing (DBR) protocol use a greedy approach to deliver data packets to the destination sink nodes at the water surface. Further, DBR does not require full-dimensional location information of sensor nodes. Instead, it needs only local depth information, which can be easily obtained with an inexpensive depth sensor that can be equipped in every underwater sensor node. DBR uses smaller depth as the only metric for choosing a route. This decision might lead to high energy consumption and long end to end delay which will degrade network performance. This paper proposes an improvement of DBR protocol by making routing decisions depend on fuzzy cost based on the residual energy of receiver node in conjunction with the depth difference of receiver node and previous forwarder node and the number of hops traveled by the received packet. Our simulation was carried out in Aquasim an NS2 based underwater simulator and the evaluation results show that the proposed fuzzy multi metric DBR protocol (FDBR) performs better than the original DBR in terms of average end to end delay, packet delivery ratio and energy savin

A New Method for Intelligent Message Network Management in Ubiquitous Sensor Networks

Ubiquitous Sensor Network (USN) computing is a useful technology forautonomic integrating in different environments which can be available anywhere.Managing USN plays an important role on the availability of nodes and paths. Inorder to manage nodes there is a cyclic route starts from manager, passing nodes,and come back to manager as feedback. In this paper, a new, self-optimizing methodpresented for finding this cyclic path by combining epsilon greedy and geneticalgorithm and then it is compared with other well-known methods in terms of cost ofthe route they find and the power consumption. The results show that the route thatis found by our new method costs at least 53% less than other methods. However insome cases, it uses 32% more energy for finding the route which can be compensatein traversing the shorter route. The overall simulation results in prototype data showthe effectiveness of the proposed method

A Comparative Study on MAC Protocols of an Underwater Surveillance System from QoS Perspective

Underwater wireless sensor networks can be employed in a vast range of applications such as the underwater surveillance systems. The adoption of underwater wireless sensor networks by underwater surveillance systems has brought forward a new challenge of fulfilling the quality of service (QoS) requirements. Providing QoS support is a challenging issue due to the highly resource constrained nature of underwater wireless sensor nodes and the harsh operation environments. In this paper, we focus on the analysis of QoS parameters for underwater surveillance system MAC protocols. In this paper, we analyze QOS parameters of four MAC protocols: RMAC, Slotted FAMA, UWALOHA and AQTUWMAC in terms of energy efficiency, packet delivery ratio and average end to end delay in two different nodes deployment strategies. To the best of our knowledge, this is the first paper that compares MAC protocols in randomly deployment strategy and Octahedron placement strategy for underwater surveillance system. We have used the underwater wireless sensor network simulator Aqua-Sim to simulate the MAC protocols

A New HadoopBased Network Management System withPolicy Approach

In recent years with the improvement in the field of network technology and decreasing of technology cost, lots of data are produced. This massive amount of data needs mechanism for processing and mining information rapidly. In this paper a new Hadoop based network management system with policy approach which is considered hierarchical manager is presented. Storing and processing massive data efficiently are two capability of Hadoop technology by using HDFS and MapReduce. In this paper, processing time is considered as a main factor. As a result it is proved that this management system using policy approach increases the performance of entire system without putting on extra cost for implementation. This system in contrast with pure Hadoop and centralized system is several times more rapid

RPL LOAD BALANCING IN INTERNET OF THINGS

ABSTRACT:  The wide address space provided by Internet Protocol version 6 (IPv6) lets any thing to be identified uniquely. consistency of the modified version of IPv6 protocol stack with smart objects, facilitated the Internet interconnection of the networks of smart objects and introduced Internet of things. A smart object is a small micro-electronic device that consists of a communication device, a small microprocessor and a sensor or an actuator. A network made of such devices is called low-power and lossy network. RPL routing protocol that is consistent to IPv6, is designed to be used in these kinds of networks. Load balancing is not considered in the RPL design process. Whenever RPL is used in large scale low-power and lossy networks some nodes will suffer from congestion and this problem severely degrades network performance. In this paper, we consider solutions provided to tackle RPL load balancing problems. Load balancing algorithms and protoclos are evaluated through simulation. We evaluate IETF RPL implementation and LB-RPL method with Contiki OS Java (COOJA) simulator. They are assessed comprehensively through metrics such as Packet delivery Ratio, Average End to End delay, and Gateway Throughput. LB-RPL improves RPL in terms of Packet delivery Ratio and throughput but increases Average End to End delay. Simulations results show that RPL load balancing needs extensive works to be performed yet

Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features

In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (ANN), weighted all nearest neighbors (WANN), and k-medoid based nearest neighbors (KMNN). In our proposed methods, we can trigger the alarm if we detect an Android app is malicious. Hence, our solutions help us to avoid the spread of detected malware on a broader scale. We provide a detailed description of the proposed detection methods and related algorithms. We include an extensive analysis to asses the suitability of our proposed similarity-based detection methods. In this way, we perform our experiments on three datasets, including benign and malware Android apps like Drebin, Contagio, and Genome. Thus, to corroborate the actual effectiveness of our classifier, we carry out performance comparisons with some state-of-the-art classification and malware detection algorithms, namely Mixed and Separated solutions, the program dissimilarity measure based on entropy (PDME) and the FalDroid algorithms. We test our experiments in a different type of features: API, intent, and permission features on these three datasets. The results confirm that accuracy rates of proposed algorithms are more than 90% and in some cases (i.e., considering API features) are more than 99%, and are comparable with existing state-of-the-art solutions.Comment: 20 pages, 8 figures, 11 tables, FGCS Elsevier journa