Security and energy efficiency are critical concerns in wireless sensor network (WSN) design. This paper aims to develop an energy-efficient secure scheme against power exhausting attacks, especially the denial-of-sleep attacks, which can shorten the lifetime of WSNs rapidly. Although various media access control (MAC) protocols have been proposed to save the power and extend the lifetime of WSNs, the existing designs of MAC protocol are insufficient to protect the WSNs from denial-of-sleep attacks in MAC layer. This is attributed to the fact that the well-known security mechanisms usually awake the sensor nodes before these nodes are allowed to execute the security processes. Therefore, the practical design is to simplify the authenticating process in order to reduce the energy consumption of sensor nodes and enhance the performance of the MAC protocol in countering the power exhausting attacks. This paper proposes a cross-layer design of secure scheme integrating the MAC protocol. The analyses show that the proposed scheme can counter the replay attack and forge attack in an energy-efficient way. The detailed analysis of energy distribution shows a reasonable decision rule of coordination between energy conservation and security requirements for WSNs.

Accurate weather data are important for planning our day-to-day activities. In order to monitor and predict weather information, a two-phase weather management system is proposed, which combines information processing, bus mobility, sensors, and deep learning technologies to provide real-time weather monitoring in buses and stations and achieve weather forecasts through predictive models. Based on the sensing measurements from buses, this work incorporates the strengths of local information processing and moving buses for increasing the measurement coverage and supplying new sensing data. In Phase I, given the weather sensing data, the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model are trained and verified using the data of temperature, humidity, and air pressure of the test environment. In Phase II, the trained learning model is applied to predict the time series of weather information. In order to assess the system performance, we compare the predicted weather data with the actual sensing measurements from the Environment Protection Administration (EPA) and Central Weather Bureau (CWB) of Taichung observation station to evaluate the prediction accuracy. The results show that the proposed system has reliable performance at weather monitoring and a good forecast for one-day weather prediction via the trained models.

Soldier-based simulators have been attracting increased attention recently, with the aim of making complex military tactics more effective, such that soldiers are able to respond rapidly and logically to battlespace situations and the commander’s decisions in the battlefield. Moreover, body area networks (BANs) can be applied to collect the training data in order to provide greater access to soldiers’ physical actions or postures as they occur in real routine training. Therefore, due to the limited physical space of training facilities, an efficient soldier-based training strategy is proposed that integrates a virtual reality (VR) simulation system with a BAN, which can capture body movements such as walking, running, shooting, and crouching in a virtual environment. The performance evaluation shows that the proposed VR simulation system is able to provide complete and substantial information throughout the training process, including detection, estimation, and monitoring capabilities.

This paper proposes a decentralized algorithm for organizing an ad hoc sensor network into clusters with directional antennas. The proposed autonomous clustering scheme aims to reduce the sensing redundancy and maintain sufficient sensing coverage and network connectivity in sensor networks. With directional antennas, random waiting timers, and local criterions, cluster performance may be substantially improved and sensing redundancy can be drastically suppressed. The simulation results show that the proposed scheme achieves connected coverage and provides efficient network topology management.

A fundamental problem when locating sensors in a network is to estimate the distance between pairs of sensors. This paper considers a variety of time-of-arrival and phase-shift approaches that use bidirectional signaling to bypass the need for accurate synchronous clocking. The measurement techniques are simulated and analyzed to assess the accuracy of the distance estimation. The analysis demonstrates tradeoffs between the accuracy of the oscillators, the accuracy of the subsequent distance estimation, and the complexity of the methods

We propose a decentralized algorithm for organizing an ad hoc sensor network into clusters. Each sensor uses a random waiting timer and local criteria to determine whether to form a new cluster or to join a current cluster. The algorithm operates without a centralized controller, it operates asynchronously, and does not require that the location of the sensors be known a priori. Simplified models are used to estimate the number of clusters formed, and the energy requirements of the algorithm are investigated. The performance of the algorithm is described analytically and via simulation.