• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.8 no.2
• /
• pp.29-34
• /
• 2008

In this paper, we proposed a system protocol for detecting injurious insect to support energy saving transmission in wireless bio sensor networks. The main ideas and features of the system are as follows. First, the route establishment method which is based on the energy efficiency and stability by using time-division tree structure. Second, multi-hop direction-based data gatering structure. In this structure, the selected fading method is used to transmit packet via the established tree structure for supporting power saving and route lifetime efficiently. Finally, we can get the node power saving and reduce transmission delay, thus network lifetime and efficiency are improved. The performance evaluation of the proposed protocol is via OPNET(Optimized Network Engineering Tool).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.10
• /
• pp.2473-2492
• /
• 2012

Energy conservation is a vital issue in wireless sensor networks. Recently, employing mobile sinks for data gathering become a pervasive trend to deal with this problem. The sink can follow stochastic or pre-defined paths; however the controlled mobility pattern nowadays is taken more into consideration. In this method, the sink moves across the network autonomously and changes its position based on the energy factors. Although the sink mobility would reduce nodes' energy consumption and enhance the network lifetime, the overhead caused by topological changes could waste unnecessary power through the sensor field. In this paper, we proposed EEDARS, an energy-efficient dual-sink algorithm with role switching mechanism which utilizes both static and mobile sinks. The static sink is engaged to avoid any periodic flooding for sink localization, while the mobile sink adaptively moves towards the event region for data collection. Furthermore, a role switching mechanism is applied to the protocol in order to send the nearest sink to the recent event area, hence shorten the path. This algorithm could be employed in event-driven and multi-hop scenarios. Analytical model and extensive simulation results for EEDARS demonstrate a significant improvement on the network metrics especially the lifetime, the load and the end-to-end delay.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.9A
• /
• pp.874-881
• /
• 2010

Acoustic signal, which is a main carrier of underwater communication, attenuates along the traveled path heavily depending on the frequency as well as inter-node distance. In addition, since it has a long propagation delay, the conventional medium access control (MAC) schemes requiring complex signaling procedures and accordingly heavy overhead messages would not be appropriate in underwater communications. In this paper, we propose a code division multiple access (CDMA) scheme as a solution for MAC of underwater communication and evaluate the performance. A hierarchical data-gathering tree topology is considered and a staggered wake-up pattern is employed for the purpose of energy saving. As a performance measure, the data rate at each level of hierarchical topology is derived.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.91-102
• /
• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Journal of Korea Multimedia Society
• /
• v.6 no.7
• /
• pp.1266-1276
• /
• 2003

Multimedia information containing voice and image is transmitted on Internet, which is ten times or hundred times larger than ordinary information. Analysis types for network management in this environment consist of a real time analysis, a basic analysis and an intensive analysis. The intensive analysis is useful for gathering the trend information of specific objects periodically for certain period in order to monitor network status. When SNMP is applied to collect the trend information of intensive analysis, it brings on the increase of network load, the delay of response time and the decrease of data collection accuracy since an agent responds to manager's every polling. In this paper, an efficient SNMP is proposed and implemented to add time variables in the existing SNMP PDU. It minimizes unnecessary traffic in the intensive analysis between manager and agent, and collects trend information more accurately. The results of experiments show that it has compatibility with the existing SNMP, decreases the amount of network traffic greatly and increases the accuracy of data collection.