Current Issue : April - June Volume : 2018 Issue Number : 2 Articles : 6 Articles
Wireless rechargeable sensor networks are becoming crucial and important in recent years for the advancement of\nwireless energy transmission technology. The previous research showed that not all of sensors can be recharged due\nto the limitation of energy capacity that mobile chargers can carry. If a sensor playing a critical role in a sensing task\ncannot function as usual due to exhausted energy, then the sensing task will be interrupted. Therefore, this paper\nproposes a novel recharging mechanism taking the coverage of sensors into consideration such that mobile chargers\ncan recharge the sensor with a high coverage degree and the network lifetime can be efficiently sustained. The\ncoverage degree of each sensor depends on its contribution to the sensing task, including the coverage and\nconnectivity capabilities. Based on the coverage degree, the sensor with a higher coverage degree will be properly\nrecharged to extend the network lifetime. Simulation results show that the proposed mechanism performs better\nagainst the related work in network lifetime....
The developments in wireless sensor network (WSN) that enriches with the unique capabilities of cognitive radio\ntechnique are giving impetus to the evolution of Cognitive Wireless Sensor Network (CWSN). In a CWSN, wireless\nsensor nodes can opportunistically transmit on vacant licensed frequencies and operate under a strict interference\navoidance policy with the other licensed users. However, typical constraints of energy conservation from batterydriven\ndesign, local spectrum availability, reachability with other sensor nodes, and large-scale network architecture\nwith complex topology are factors that maintain an acceptable network performance in the design of CWSN. In\naddition, the distributed nature of sensor networks also forces each sensor node to act cooperatively for a goal of\nmaximizing the performance of overall network. The desirable features of CWSN make Multi-agent Reinforcement\nLearning (RL) technique an attractive choice. In this paper, we propose a reinforcement learning-based transmission\npower and spectrum selection scheme that allows individual sensors to adapt and learn from their past choices and\nthose of their neighbors. Our proposed scheme is multi-agent distributed and is adaptive to both the end-to-end\nsource to sink data requirements and the level of residual energy contained within the sensors in the network. Results\nshow significant improvement in network lifetime when compared with greedy-based resource allocation schemes....
The wireless sensor network is an emerging technology that has numbers of applications including environmental monitoring, surveillance, biomedical systems and robotic exploration. Typically, the sensor nodes consist of a sensor(s) to monitor the surrounding environmental conditions, a processing unit and a transceiver unit to communicate with other nodes and an on board power supply. Reporting constant measurement updates of temperature incurs high communication costs for each node, resulting in a significant communication overhead and energy consumption. A solution to reduce power requirement is to transmit, among all constant data measured by the sensor, the transmitter transmits only one measurement and it becomes ON only when temperature changes occur. Wireless Sensor Node has also been designed and implemented using VHDL on Spartan 3 FPGA to exhibit the functionality of the node’s efficient transmitter and receiver before the actual deployment. This paper describes the implementation of a Run-Length data compression algorithm for WSN, on a Xilinx Spartan-3 FPGA. A temperature sensor has been used to sense the atmospheric temperature which serves as an input to the transmitter via the microcontroller ATmega8L. It performs analog to digital conversion of the data sensed by the temperature sensor with the help of internal ADC. The digital data is transmitted after Run-Length Encoding Compression. The decompression takes place at the receiver node. The FPGA implementation of the system and the simulation results of all the modules have been presented....
We study simultaneous wireless information and power transfer (SWIPT) in multihop wireless cooperative networks, where the\nmultihop capability that denotes the largest number of transmission hops is investigated. By utilizing the broadcast nature of\nmultihop wireless networks, we first propose a cooperative forwarding power (CFP) scheme. In CFP scheme, the multiple relays\nand receiver have distinctly different tasks. Specifically, multiple relays close to the transmitter harvest power from the transmitter\nfirst and then cooperatively forward the power (not the information) towards the receiver. The receiver receives the information\n(not the power) from the transmitter first, and then it harvests the power from the relays and is taken as the transmitter of the next\nhop. Furthermore, for performance comparison, we suggest two schemes: cooperative forwarding information and power (CFIP)\nand direct receiving information and power (DFIP). Also, we construct an analysis model to investigate the multihop capabilities\nof CFP, CFIP, and DFIP schemes under the given targeted throughput requirement. Finally, simulation results validate the analysis\nmodel and show that the multihop capability of CFP is better than CFIP and DFIP, and for improving the multihop capabilities, it\nis best effective to increase the average number of relay nodes in cooperative set...
In this paper, a resource allocation algorithm in two-way orthogonal frequency division multiplexing (OFDM) based\ncognitive radio networks with quality of experience (QoE) and power consumption guarantees is proposed. We define\nthe overall QoE perceived by secondary users (SUs) per power consumption as QoEW. The power consumption\nmodel consists of fixed circuit power, dynamic circuit power, and transmit power which depends on the efficiency of\nthe power amplifiers at different terminals. Under the constraint of total maximum transmit power, the optimization\nobjective is to maximize QoEW while meeting the minimum QoE demands of SUs and maintaining interference\nthreshold limitations of multiple primary users. The resource allocation problem is formulated into a nonlinear\nfractional programming and transformed into an equivalent convex optimization problem via its hypograph form.\nBased on the Lagrange dual decomposition method and cross-layer (CL) optimization architecture, this convex\noptimization problem is separately solved in the physical layer and the application layer. The optimal QoEW is\nachieved through the proposed CL alternate iteration algorithm. Numerical simulation results demonstrate the\nimpacts of system parameters on QoEW and the effectiveness and superiority of the proposed algorithm....
This paper studies the cognitive wireless powered device-to-device (D2D) communication underlaying a cellular\nnetwork, where there are two types of communications, including peer-to-peer (P2P) communication and a\nmulti-hop D2D communication, which are assumed to be affected by hardware impairments (HWIs). We investigate\nthe one-hop P2P communication and the multi-hop D2D communication, where the relay node helps other two user\nequipments (UEs) exchange information with a two-time-slot physical-layer network coding scheme. To examine the\nsystem performance, we derive closed-form expressions for the average energy efficiency (EE) and spectral efficiency\n(SE). Besides that, we also obtain the successful transmission probability (STP) for the two considered communications,\nand the optimal values of time switching (TS) and power splitting (PS) ratios are achieved with the help of a genetic\nalgorithm (GA)-based optimization algorithm in our proposed energy harvesting (EH) protocol so-called Hybrid TS-PS\n(HTPS) relaying protocol. Comparisons between amplify-and-forward (AF) and decode-and-forward (DF) transmission\nschemes are provided. The simulation results give evidence that the STP is significantly improved regardless of the\npresence of HWIs thanks to the derived parameters, i.e., average EE, SE, and the optimal TS and PS ratios....
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