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The context refers to “any information that can be used to characterize the situation of an entity, where an entity can be a person, place, or physical object.” Radio context awareness is defined as the ability of detecting and estimating a system state or parameter, either globally or concerning one of its components, in a radio system for enhanci...
Citations
... Wireless Sensor Networks (WSNs) have been extensively investigated for various fields, e.g., agriculture and environmental sensing [1,2]. In wild environments, sensor nodes, despite their limited battery capacity, are not attended to (no recharge, no battery exchange) any more once deployed. ...
In large-scale wireless sensor networks (WSNs), nodes close to sink nodes consume energy more quickly than other nodes due to packet forwarding. A mobile sink is a good solution to this issue, although it causes two new problems to nodes: (i) overhead of updating routing information; and (ii) increased operating time due to aperiodic query. To solve these problems, this paper proposes an energy-efficient data collection method, Sink-based Centralized transmission Scheduling (SC-Sched), by integrating asymmetric communication and wake-up radio. Specifically, each node is equipped with a low-power wake-up receiver. The sink node determines transmission scheduling, and transmits a wake-up message using a large transmission power, directly activating a pair of nodes simultaneously which will communicate with a normal transmission power. This paper further investigates how to deal with frame loss caused by fading and how to mitigate the impact of the wake-up latency of communication modules. Simulation evaluations confirm that using multiple channels effectively reduces data collection time and SC-Sched works well with a mobile sink. Compared with the conventional duty-cycling method, SC-Sched greatly reduces total energy consumption and improves the network lifetime by 7.47 times in a WSN with 4 data collection points and 300 sensor nodes.
... The signal often decays at an uncertain rate. 40,41 Besides, different transmitting antennas have various gains and wavelengths. 42 A simplified form of the relationship is defined ...
The surveillance system, which is mainly used for detecting and tracking moving targets, is one of the most significant applications of wireless sensor networks. Up to present, received signal strength indicator is the most common measuring mean for estimating the distance in sensor networks. However, in the presence of noise, it is impossible to gain the accurate distance based on received signal strength indicator. In this article, we propose a new tracking scheme based on received signal strength difference, which is the difference value of received signal strength indicators between two neighboring sampling steps. Supposing the noise has a certain degree of correlation in a certain time interval, received signal strength difference can effectively reduce the negative impact from noise. The tracking algorithm based on received signal strength difference is built: The sensor nodes collectively estimate a possible zone of the target via the signs of received signal strength difference. Next, the possible zone is further immensely shrunk to the refined zone via the absolute values of received signal strength difference. Finally, we determine the target’s final location by choosing the reference dot with the minimum norm in the refined zone. The simulation results demonstrate that the proposed tracking method achieves higher localization accuracy than the typical received signal strength indicator–based scheme. The received signal strength difference–based method also has good generality and robustness with respect to the noises with different deviation values and the target following arbitrarily state model.
... The context of the radio can be spectrum usage, channel state information, energy consumption, hardware impairment level and specific QoS demand of the application. Those contexts can be used to enhance the performance of the system or to improve the effectiveness of application [1]. ...
Cognitive radio is an intelligent radio which will run the cognitive cycle of observing, understand, create knowledge, make a decision and modifies the radio parameters for the given objective. Cognitive radio designed with single purpose may not be suitable for the next generation of heterogeneous network, where there are multiple QoS requirements on application/user side, experiences a different kind of channel condition and must support different frequency band of transmission. So, there is a need for cognitive radio that will meet the multi-scenario requirements or context aware cognitive radio communication system for the heterogeneous network. This work presents five transmission mode cognitive waveforms for handle five different contexts. The five transmission waveforms are (1) Energy efficient QoS CR waveform using Genetic algorithm. (2) Low data rate FBMC based subcarrier level interleave CR waveform. (3) Emergency communication support underlay spatial coder waveform. (4) Hardware impairment handling waveform using prewhitened precoding. (5) Imperfect channel state handling adaptive training sequence design based interleave CR waveform. Optimal decision making based on observed values and receiver feedback relies on the accuracy level of observed values which is not a precise one. The fuzzy logic is tolerant of such impreciseness of data. So a cognitive engine deigns with fuzzy based decision system to select optimal waveform for the given context is presented. The system is designed to take input from spectrum hole from detecting unit and database, inputs from receiver feedback like BER, data rate, channel gain, channel imperfection, SINR from PR receiver, input from the transmitter about hardware impairment and finally input from user application about the QoS requirement.
