In applications of wireless sensor networks, there are many security issues. Attackers can create false reports and transmit the reports to the networks. These false reports can lead not only false alarms, but also the depletion of limited energy resources. In order to filter out such false reports during the forwarding process, Ye et al. proposed the statistical en-route filtering (SEF). Several research efforts to enhance the efficiency of SEF have been made. Especially, the path selection method proposed by Sun et al. can improve the detection power of SEF by considering the information on the filtering keys of and distances of upstream paths. However, such selection mechanism could lead to favored paths in heavy traffic, which would result in unbalanced energy consumption. In this paper, we propose a path renewal method to provide load balancing for sensor networks in terms of energy consumption. In our method, a node renews its upstream path to save energy resources if the remaining energy of and the communication traffic of the node exceed some threshold values. We show the effectiveness of the proposed method in terms of balanced energy consumption and filtering power by providing simulation results.
Cyber-physical systems (CPS) are composed of collaborating computational elements that control physical entities. The human desire to acquire useful information and to automatically control devices anytime and anywhere has increased the necessity to develop systems with high reliability. However, the CPS associated with the physical world and cyber world involve managing complexity and incurring the respective maintenance costs, such that it has been impossible to produce systems that are completely reliable. Thus, this paper presents an 'Autonomic Control System for High-reliable Cyber-Physical Systems' that is comprised of eight processes, including fault analysis, fault event analysis, fault modeling, fault state interpretation, fault strategy decision, fault detection, diagnosis & reasoning, and maneuver execution. These processes facilitate the design and implementation of an autonomic control system that is superior to what was available before. As proof of the approach, we used an intelligent service robot (ISR) as a case study. The experimental results show that the ISR is able to detect a fault event during autonomic control of the CPS.
There has been a growing interest in the applications of sensor networks, such as combat field surveillance, terrorist tracking and highway traffic monitoring. These applications gather sensed data from sensor nodes to monitor events in the territory of interest. One of the important issues in these applications is the existence of the radio-jamming zone between source nodes and the base station. Depending on the routing protocol the transmission of the sensed data may not be delivered to the base station. To solve this problem we propose a routing path generation method that is based on genetic algorithms for reliable transmission by considering the balanced energy depletion of the sensor nodes. The genetic algorithm finds an efficient routing path by considering the radiojamming zone, energy consumption needed for data transmission and average remaining energy. The fitness function employed in the genetic algorithm is implemented by applying the fuzzy logic. In simulation, our proposed method is compared with LEACH and Hierarchical PEGASIS. The simulation results show that the proposed method is efficient in both the energy consumption and success ratio of the delivery.
CPS (cyber-physical systems) which consists of connected and diverse embedded systems and physical systems are a new paradigm. Traditional systems were usually considered to be passive and dumb parts in physical systems, but with CPS, we have to take into account what are being moved or changed in the physical systems. So, as increasing the complexity of CPS, potential errors in the systems also increase. In this paper, for enhancing the reliability of CPS, we exploit an executable-model-based design methodology and propose a distributed simulation method to verify the design of CPS. For the design of the systems including discrete and continuous factors, we apply DEV&DESS formalism and simulate models in distributed simulation environments through DDS middleware. We also illustrate the applications of CPS with our modeling tool.
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