Time Synchronization Of Nodes Research Articles

Discovery of Random Delay Distribution Based on Complex WSNs Clock Synchronization

With the rapid development, Wireless Sensor Networks (WSNs) technology has been widely applied in many fields, such as military intelligence detection, medical condition detection, urban facility construction and smart home, etc. Clock synchronization is one of the key technologies in WSNs, precise node time synchronization is critical for many networked control applications in WSNs. Once the clock of sensor nodes loses synchronization, the information collected by nodes will lose timeliness and value for users. Therefore, the key technology of clock synchronization plays an important role in WSNs. The main purpose of clock synchronization is to control the interval time that between sending and receiving timestamps of wireless sensor nodes within acceptable clock synchronization accuracy. However, because there are many kinds of link delays in the process of information transmission, the transmission time is prolonged and the time precision is reduced. Therefore, it is necessary to study the exact causes and effects of these errors. In ZigBee wireless sensor network, through theoretical model and data experimental analysis, we got the result that the random delay distributions of the uplink and downlink obey normal distribution when the sensor nodes exchange some information.

Mobile target localization based on iterative tracing for underwater wireless sensor networks

In underwater wireless sensor networks, sensor position information has important value in network protocols and collaborative detection. However, many challenges were introduced in positioning sensor nodes due to the complexity of the underwater environment. Aiming at the problem of the stratification effect of underwater acoustic waves, the long propagation delay of messages, as well as the mobility of sensor nodes, a mobile target localization scheme for underwater wireless sensor network is proposed based on iterative tracing. Four modules are established in the mobile target localization based on iterative tracing: the data collection and rough position estimation, the estimation and compensation of propagation delay, the node localization, and the iteration. The deviation of distance estimation due to the assumption that acoustic waves propagate along straight lines in an underwater environment is compensated by the mobile target localization based on iterative tracing, and weighted least squares estimation method is used to perform linear regression. Moreover, an interacting multiple model algorithm is put forward to reduce the positioning error caused by the mobility of sensor nodes, and the two services of node time synchronization and localization assist each other during the iteration to improve the accuracy of both parties. The simulation results show that the proposed scheme can achieve higher localization accuracy than the similar schemes, and the positioning errors caused by the above three problems can be reduced effectively.

Energy-Efficient Protocol for Deterministic and Probabilistic Coverage in Sensor Networks

Various sensor types, e.g., temperature, humidity, and acoustic, sense physical phenomena in different ways, and thus, are expected to have different sensing models. Even for the same sensor type, the sensing model may need to be changed in different environments. Designing and testing a different coverage protocol for each sensing model is indeed a costly task. To address this challenging task, we propose a new probabilistic coverage protocol (denoted by PCP) that could employ different sensing models. We show that PCP works with the common disk sensing model as well as probabilistic sensing models, with minimal changes. We analyze the complexity of PCP and prove its correctness. In addition, we conduct an extensive simulation study of large-scale sensor networks to rigorously evaluate PCP and compare it against other deterministic and probabilistic protocols in the literature. Our simulation demonstrates that PCP is robust, and it can function correctly in presence of random node failures, inaccuracies in node locations, and imperfect time synchronization of nodes. Our comparisons with other protocols indicate that PCP outperforms them in several aspects, including number of activated sensors, total energy consumed, and network lifetime.