Wireless sensor network is a collection of small devices called sensors nodes, which are deployed in the sensing field to monitor physical and environmental information. Location information of sensor node is a critical issue for many applications in wireless sensor network. The main problem is to design a path for a mobile landmark to maximize the location accuracy as well as to reduce energy consumption. Different path planning schemes have been proposed for localization.Here, this study focused only on static path planning scheme. In this article, the performance of five static path planning schemes is evaluated, namely, random way point, Scan, D-Scan, Hilbert, and Circles based on three parameters such as location error ratio, energy consumption, and number of references. Network simulator-2 is used as a simulation tool. Simulation scenarios with three node densities are used in this research study such as sparse node density, medium node density, and dense node density. The analysis of simulation results concludes that random way point has higher performance efficiency compared to rest of the static path planning algorithms concerning location error ratio (accuracy), energy consumption, and number of references in medium and dense node density scenarios. Hilbert performance was found good only in sparse node density scenario.
Malicious node invasion as black hole attack is a burning issue in MANETs. Black hole attacks with a single malicious node is easy to detect and prevent. The collaborative attacks with multiple cooperative malicious node is a challenging issue in security of MANETs as it is difficult to figure out due to its complex and sophisticated mechanism. This study proposed a novel signature-based technique to detect and handle the cooperative black hole attack in MANETs. For this purpose, diverse type of simulation scenarios are used with increasing number of nodes. The parameters such as average throughput, average packet drop, average end to end delay, average processing time and malicious node detection rate are used to measure the impact of signature-based malicious node detection scheme. AODV is used as routing protocol in this study. This study revealed that the performance of MANETs degrades with an increase in a number of malicious nodes. The average throughput of MANETs decreases with increase in average end to end delay and average packet drop. Signature-based malicious nodes detection mechanism is used to counter the cooperative black hole attack. The signature-based technique has enhanced the detection and elimination of cooperative black hole attack in MANETs. This helps in comparatively an increase in average throughput and decrease in packet delay and packet drop.
The importance of Wireless Sensor Network (WSN) increases due to deployment for geographical, environmental and surveillance purpose in war fields. WSN facing several challenges due to its complex nature including key problems, such as routing and medium access control protocols. Several approaches were proposed for the performance evaluation of WSN on the basis of these issues due to the fact that MAC layer access protocols have a great impact on the performance of WSN. In this paper, we investigated the performance evaluation of three well known MAC Access protocols, i.e. sensor medium access control protocol (SMAC), carrier sense multiple access with collision avoidance (CSMA/CA), and time division multiple access (TDMA) over adhoc on demand distance vector (AODV) routing protocol. The number of simulation scenarios were carried out by using NS-2, the simulation metrics used are throughput, end-to-end delay and energy consumed. Simulation results showed that SMAC out perform CSMA/CA and TDMA by consuming less energy, less end to end delay and high throughput due to contention based approach to access the medium for transmission. Keywords-Medium access control (MAC); sensor medium access control (SMAC); time division multiple access (TDMA); carrier sense multiple access with collision avoidance (CSMA/CA); ad-hoc on demand distance vector (AODV)
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