With the rapid development and popularization of Internet, and the advent of new computing technologies, new kind of application scenario with characteristics different from the traditional computing model is emerging in Internet of Things (IoT). In this scenario, a massive amount of data processing or service requests with higher reliability and quality of service (QoS) will be generated in a short period of time, which is called real-time massive services (RMS) request in this paper. Generally, RMS requests are not handled well with current service provision mechanism. For solving this problem, a new mechanism for IoT service provision based on transient private cloud is proposed. The basic idea of our mechanism is classifying service requests and mobilizing massive amount resources by transient private cloud. Based on the proposed mechanism, we design a framework and further illustrate each component of the framework as well as the process of service distribution.
Energy recharging has received much attention in recent years. Several recharging mechanisms were proposed for achieving perpetual lifetime of a given Wireless Sensor Network (WSN). However, most of them require a mobile recharger to visit each sensor and then perform the recharging task, which increases the length of the recharging path. Another common weakness of these works is the requirement for the mobile recharger to stop at the location of each sensor. As a result, it is impossible for recharger to move with a constant speed, leading to inefficient movement. To improve the recharging efficiency, this paper takes "recharging while moving" into consideration when constructing the recharging path. We propose a Recharging Path Construction (RPC) mechanism, which enables the mobile recharger to recharge all sensors using a constant speed, aiming to minimize the length of recharging path and improve the recharging efficiency while achieving the requirement of perpetual network lifetime of a given WSN. Performance studies reveal that the proposed RPC outperforms existing proposals in terms of path length and energy utilization index, as well as visiting cycle.
Coverage is an important issue that has been widely discussed in wireless sensor networks (WSNs). However, it is still a big challenge to achieve both purposes of full coverage and energy balance. This paper considers the area coverage problem for a WSN in which each sensor has a variable sensing radius. To prolong the network lifetime, a weighted Voronoi diagram (WVD) is proposed as a tool for determining the responsible sensing region of each sensor according to the remaining energy in a distributed manner. The proposed mechanism, which is called sensing radius adaptation (SRA), mainly consists of three phases. In the first phase, each sensor and its neighboring nodes cooperatively construct the WVD to identify the responsible monitoring area. In the second phase, each sensor adjusts its sensing radius to reduce the overlapping sensing region such that the purpose of energy conservation can be achieved. In the last phase, the sensor with the least remaining energy further adjusts its sensing radius with its neighbor for to maximize the network lifetime. Performance evaluation and analysis reveal that the proposed SRA mechanism outperforms the existing studies in terms of the network lifetime and the degree of energy balance.
Barrier coverage is a fundamental issue in wireless sensor networks (WSNs). Most existing works have developed centralized algorithms and applied the Boolean Sensing Model (BSM). However, the critical characteristics of sensors and environmental conditions have been neglected, which leads to the problem that the developed mechanisms are not practical, and their performance shows a large difference in real applications. On the other hand, the centralized algorithms also lack scalability and flexibility when the topologies of WSNs are dynamically changed. Based on the Elfes Sensing Model (ESM), this paper proposes a distributed Joint Surveillance Quality and Energy Conservation mechanism (JSQE), which aims to satisfy the requirements of the desired surveillance quality and minimize the number of working sensors. The proposed JSQE first evaluates the sensing probability of each sensor and identifies the location of the weakest surveillance quality. Then, the JSQE further schedules the sensor with the maximum contribution to the bottleneck location to improve the overall surveillance quality. Extensive experiment results show that our proposed JSQE outperforms the existing studies in terms of surveillance quality, the number of working sensors, and the efficiency and fairness of surveillance quality. In particular, the JSQE improves the surveillance quality by 15% and reduces the number of awake sensors by 22% compared with the relevant TOBA.
In this paper, we combine the frequency selective surface (FSS) with the polarization conversion surface to realize the frequency selective polarization converter (FSPC), which can be used to reduce the backward radar cross section. The proposed FSPC consists of a curved double-arrow reflection polarization conversion layer in the upper layer and a chamfer FSS layer in the lower layer. The results show that the transparent frequency band of 3-dB ranges from 8.9 to 14 GHz, and the relative bandwidth of the co-polarizing reflection coefficient less than-10dB is 135%. The reflection polarization conversion rate of the designed FSPC is over 90% in the frequency bands of 4-7.9 GHz and 16.3-20 GHz on both sides of the passband. Furthermore, the designed FSPC is arranged on a chessboard pattern to reduce backscattering energy in the broadband range from 3.5 to 23 GHz. Compared with traditional polarization converters, the designed FSPC exhibits an extra frequency selection performance of a broadband transmission window. These results will have potential applications in radomes to achieve wideband low backward RCS.
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