The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN) applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free, distributed and probabilistic Mobile Beacon-assisted Localization (MBL) approach for static WSNs. Then, we propose another approach based on MBL, called Adapting MBL (A-MBL), to increase the efficiency and accuracy of MBL by adapting the size of sample sets and the parameter of the dynamic model during the estimation process. Evaluation results show that the accuracy of MBL and A-MBL outperform both Mobile and Static sensor network Localization (MSL) and Arrival and Departure Overlap (ADO) when both of them use only a single mobile beacon for localization in static WSNs.

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As many Wireless Sensor Networks (WSNs) applications require sensor position information, localization has been an important problem in WSNs. To reduce the number of seeds, a number of mobile-assisted approaches have been proposed. Current proposed mobile-assisted approaches for localization require special hardware or face route selection problem, however. In this paper, we propose a Mobile-Assisted Monte Carlo Localization (MA-MCL) for WSNs. Our approach relies on direct arriver and leaver information from a single mobile-assisted seed. It does not require any specially designed hardware due to the range-free technique, and the single mobile-assisted seed in our approach can move uncontrollably to avoid route selection problem based on Monte Carlo method. Evaluation results show that the accuracy of MA-MCL outperforms MSL * , MSL, and ADO when all of them use only a mobile seed for localization in the static sensor networks.

Localization is one of the most important subjects in Wireless Sensor Networks (WSNs). To reduce the number of beacons and adopt probabilistic methods, some particle filter-based mobile beacon-assisted localization approaches have been proposed, such as Mobile Beacon-assisted Localization (MBL), Adapting MBL (A-MBL), and the method proposed by Hang et al. Some new significant problems arise in these approaches, however. The first question is which probability distribution should be selected as the dynamic model in the prediction stage. The second is whether the unknown node adopts neighbors’ observation in the update stage. The third is how to find a self-adapting mechanism to achieve more flexibility in the adapting stage. In this paper, we give the theoretical analysis and experimental evaluations to suggest which probability distribution in the dynamic model should be adopted to improve the efficiency in the prediction stage. We also give the condition for whether the unknown node should use the observations from its neighbors to improve the accuracy. Finally, we propose a Self-Adapting Mobile Beacon-assisted Localization (SA-MBL) approach to achieve more flexibility and achieve almost the same performance with A-MBL.

Data memory has been a very scarce resource in sensor networks. Thus, its efficient utilization is necessary. There cannot exist a memory allocator that will deliver best performance and least memory consumption for all programs, but a particular allocator that works best for a particular program is possible. In this paper we present SDMA, a simulation-driven dynamic memory allocator for wireless sensor networks. SDMA can choose a 'best' allocator by simulator and analyzer for a particular user program among allocator candidates. We also show the significance of some factors which effect determination of allocator candidates, and then give a quantitative formula for analyzer. In our evaluation, we show that our scheme will deliver better performance and less memory consumption than only single memory allocator in the current WSN OS.

Determining the physical positions of sensors has been a fundamental and crucial problem in wireless sensor networks (WSNs). Due to the inherent characteristics of WSNs, extremely limited resources available at each low-cost and tiny sensor node, connectivity-based range-free solutions could be a better choice to feature a low overall system cost. However, localization by means of mere connectivity may underutilize the proximity information available from neighborhood sensing. Although received signal strength (RSS) values are irregular and highly dynamic, it might provide heuristic information about which neighboring nodes are closer and which are further. We propose a distributed Mobile-beacon-assisted localization scheme based on RSS and Connectivity observations (MRC) with a specific trajectory in static WSNs. To ensure that MRC performs as true to reality as possible, we propose two improved approaches based on MRC to consider irregular radio scenario in the noisy environment. Comparing the performance with three typical range-free localization methods in static WSNs, our lightweight MRC algorithm with limited computation and storage overhead is more suitable for very low-computing power sensor nodes that can efficiently outperform better with only basic arithmetic operations in the simulation and physical environments.

In recent years, Wireless Sensor Networks (WSNs) have been an area of significant research. The challenges regarding programming, monitoring, managing WSNs are increasing dramatically, and challenges for the corresponding tools as well. This paper covers the whole scope of the WSN development life cycle and describes the major components for developing WSNs, then provides a survey of some of the software tools currently available to assist in the development of WSNs, and finally presents challenges for developing WSNs. These challenges for future development tools of WSNs provide some motivations and objectives.

Abstract. Train operation control system is the core of urban rail transit system, and the train carborne control system is an important part of the whole train operation control systems, and its reliability will directly affect the safety of the train. In this paper, two out of three closed-loop dynamic input detection technology and hardware dynamic two out of two voting output technology and time synchronization technology were used to design relay node status acquisition and control scheme of the train carborne control system respectively, and effectively guaranteed the reliability of relay node status acquisition and control of the train carborne control system and therefore ensure the safety of operation of the train.