Fuzzy-Oriented Dynamic Energy Allocation Strategies for Wireless Sensor Networks

To improve the service efficiency of planar automated multi-storey parking systems, a nonlinear dynamic slot allocation strategy is proposed, considering both vehicle dwell time and the balance of slot distribution. A long short-term memory (LSTM) neural network, with hyperparameters tuned by Bayesian optimization, is employed to predict the dwell times of vehicles parked in the system. Combining predicted dwell times with slot occupancy attributes, a fuzzy-theory-based partitioning rule is established, enabling nonlinear regional allocation based on dwell time and slot occupancy density. Additionally, a balance-detection algorithm and an adjacent-slot migration strategy are designed to assess the balance of slot utilization and dynamically adjust partitioned slot occupancy. Using the average running energy consumption and average service time of the rail-guided vehicle (RGV), which performs vehicle storage and retrieval operations, as system efficiency metrics, a tabu search algorithm is applied to optimize the selection of vacant slots within each partition. Results show that, compared with the nearest-slot allocation strategy commonly used in engineering practice, the proposed method reduces the average service time by 14.3%, the average energy consumption by 23.8%, and the standard deviation of slot-occupancy balance by 90.28%, demonstrating the effectiveness of the dynamic slot allocation strategy that accounts for vehicle dwell time and balanced slot distribution.

In this letter, we investigate the dynamic rate allocation and forwarding strategy adaption scheme for wireless networks with potential selfish nodes. Aided by an incentive mechanism, we develop a stochastic differential equation (SDE) to portray the dynamic node selfishness in terms of node's energy resource and incentives. Then, a stochastic optimization model is employed to maximize the average network utility while bounding the node selfishness. Based on the continuous-time Lyapunov optimization theory, we solve the optimization problem and propose a dynamic rate allocation and forwarding strategy update (DRAF) algorithm to accommodate the dynamic network state. We further analyze the tracking errors between the output of DRFA algorithm and the optimal solution. Then, an adaptive-compensation rate allocation and forwarding strategy update (ACRAF) algorithm is designed, which iterates only once when network state changes. Finally, we provide a sufficient condition that the ACRAF algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis.

In video-on-demand (VOD) systems, as the size of the buffer allocated to user requests increases, initial latency and memory requirements increase. Hence, the buffer size must be minimized. The existing static buffer allocation scheme, however, determines the buffer size based on the assumption that the system is in the fully loaded state. Thus, when the system is in a partially loaded state, the scheme allocates a buffer larger than necessary to a user request. This paper proposes a dynamic buffer allocation scheme that allocates to user requests buffers of the minimum size in a partially loaded state as well as in the fully loaded state. The inherent difficulty in determining the buffer size in the dynamic buffer allocation scheme is that the size of the buffer currently being allocated is dependent on the number of and the sizes of the buffers to be allocated in the next service period. We solve this problem by the predict-and-enforce strategy, where we predict the number and the sizes of future buffers based on inertia assumptions and enforce these assumptions at runtime. Any violation of these assumptions is resolved by deferring service to the violating new user request until the assumptions are satisfied. Since the size of the current buffer is dependent on the sizes of the future buffers, the size is represented by a recurrence equation. We provide a solution to this equation, which can be computed at the system initialization time for runtime efficiency. We have performed extensive analysis and simulation. The results show that the dynamic buffer allocation scheme reduces initial latency (averaged over the number of user requests in service from one to the maximum capacity) to 1 ÷ 29.4 n 1 ÷ 11.0 of that for the static one and, by reducing the memory requirement, increases the number of concurrent user requests to 2.36 ∼ 3.25 times that of the static one when averaged over the amount of system memory available. These results demonstrate that the dynamic buffer allocation scheme significantly improves the performance and capacity of VOD systems.

The routing protocol is one of the most important aspects of the wireless sensor network, determining network connectivity, delay, energy balancing, reliability and other factors. Furthermore, a minimum hop-count does not mean minimum delay and energy consumption. To overcome the routing protocol's shortcomings, we proposed an energy-efficient expected transmission count routing decision strategy (E3TX) for wireless sensor networks. E3TX makes use of two physical parameters, received signal strength indicator (RSSI) and battery voltage, to obtain the final decision via our proposed decision strategy. In our strategy, the received signal strength indicator is used to predict the future packet reception rate (PRR), and the battery voltage is used to estimate the residual energy of network nodes to balance their load. In order to estimate the packet reception rate via the received signal strength indicator with greater accuracy, we performed multiple experiments to build the relationship model between RSSI and PRR. In this article, we use NS-2.35 to evaluate and compare the performance of E3TX with AODV, AOMDV and BIETX. Our simulation results show that our proposed E3TX performs well when compared to the previous studies, not only in terms of energy consumption, but also in reliable data transmission and end-to-end delay.

This paper proposes a new backward‐type speech coding system (ADPCM‐AB) for wideband (7 kHz) speech and sound signals. In this system, a split‐band adaptive predictive coding scheme with gradient PARCOR lattice filter and a dynamic bit allocation scheme are employed, where quantization bits are dynamically allocated over the subbands (frequency), the subintervals (time), and the channels (stereo) in accordance with the distribution of the residual energies. They serve to remove the redundancies due to the periodic concentration of the prediction residual energy and the nonuniform nature of the speech spectrum. The ADPCM‐AB needs neither longer delay time than 4 ms nor transmission of side information parameters because the parameters for predictive coding and dynamic bit allocation are calculated with the locally decoded signals. It is clarified that the ADPCM‐AB system has the best speech quality among the conventional backward‐type coding systems. It is also shown that this system provides speech quality subjectively equivalent to 11‐bit linear PCM (176 kb/s) at 64 kb/s.

This article investigates resource allocation in ATM networks and emphasizes bandwidth allocation. Resource allocation methods are categorized, and static and dynamic allocation strategies are compared. Dynamic allocation uses actual traffic behavior, while static allocation uses only the reference traffic condition given a priori. Examples of dynamic allocation strategies are available bit rate flow control, dynamic connection admission control, and dynamic VP bandwidth control. Dynamic resource allocation is shown to be promising for situations where the a priori reference model is unclear.

Presents the results of an evaluation of several agent-based dynamic task allocation strategies which we have developed for information systems spread over the Internet or other wide area networks (WANs). These strategies are based on the cooperation of agents organized in one community of reactive agents and another community of allocator agents, and on the learning skills of the allocator agents. Reactive agents represent the services available on WANs such as Internet. Allocator agents are able to cooperate and to learn about the states of the reactive agents. We compare our multi-agent-based strategies with negotiation-based dynamic task allocation strategies, by simulation. The results of this evaluation show that our strategies reduce the amount of message sending.

Machine-to-Machine (M2M) communication is used for automated data transmission and measurement between remote electronic devices. Every device that can use M2M communication will become a significant component of the Internet of Things (IoT) and Big Data. And there are many different applications of M2M communication (e.g., fleet management, smart metering, smart grid, and home/factory/city automation). Machine-Type-Communication (MTC) is a new service defined by the 3GPP to enable M2M devices to communicate with each other over LTE-Advanced cellular networks. Since the number of M2M devices can be fairly large compared to the H2H devices, current RACH procedure may cause a QoS degradation of H2H devices. In this paper, we propose a novel dynamic RACH preamble allocation scheme that can adjust the range of available RACH preamble according to network condition dynamically. The dynamic RACH preamble allocation scheme can be an efficient solution that can alleviate the overload problem.

Driven by the emerging demands of enhancing the indoor coverage, the network capacity and users’ service experience, small scaled femtocell networks will be implemented extensively in home, office and hotspots, which can provide substantial benefits for both operators and users, such as the capital expenditures (CAPEX) and the operational expenditures (OPEX) reduction, the easy plug-and-play (PnP) deployment. Due to users’ random-deployed manner indoors, femto base station (FBS) should have cognitive abilities, such as the position awareness, the automatic parameter tuning and the learning ability, in order to self-optimize its indoor coverage and minimize the unexpected interference outdoors. In contrast to the traditional static power allocation algorithm, the FBS has applied the dynamic power allocation scheme to minimize the interference to macro base station (MBS) and macro user equipment (MUE) by considering the path loss under different wall penetration conditions. Based on the position awareness ability, the optimal coverage radii for FBS indoors are proposed and proved by closed-form formulas at typical positions, which were not considered before. To further enable the intelligent coverage self-optimization for the random-deployed FBS indoors, a joint dynamic power allocation and antenna pattern selection scheme has been proposed based on the Artificial Neural Network (ANN) with training and self-tuning abilities. Numerous results prove both the effectiveness and the accuracy of the proposed coverage self-optimization scheme for the random-deployed femtocell networks.KeywordsCognitive Femtocell NetworkArtificial Neural NetworkCoverage Self-optimization

Grid computing systems are a cost-effective alternative to traditional high-performance computing systems. However, the computing resources of a grid are usually far apart and connected by Wide Area Networks resulting in considerable communication delays. Hence, efficient allocation of jobs to computing resources for load balancing is essential in these grid systems. In this paper, two price-based dynamic job allocation schemes for computational grids are proposed whose objective is to minimize the execution cost for the grid users' jobs. One scheme tries to provide a system-optimal solution so that the expected price for the execution of all the jobs in the grid system is minimized, while the other tries to provide a job-optimal solution so that all the jobs in the system of the same size will be charged approximately the same expected price independent of the computers allocated for their execution to provide fairness. The performance of the proposed dynamic schemes is compared with static job allocation schemes using simulations.

In this paper, we introduce an uplink transmission model with mobile users, where each user maximizes his/her utility to achieve the best performance. We propose a power allocation scheme for each mobile user when all channel information is available. Moreover, we illustrate that one user would expect to predict the aggregate interference to maximize the utility when the channel information is incomplete. It is shown that this approach forms a game with incomplete information. We demonstrate the prediction rules that can help the mobile users dynamically adjust predictions and apply the Kalman filter to tackle measurement noises. We also illustrate the theoretical bound for the difference between the utility with prediction and that with complete information. Moreover, applying dynamic programming from control theory, we give a dynamic power allocation scheme based on the predictions. Simulation results indicate that our power allocation scheme with complete information and dynamic power allocation with predictions give better performance compared with the scheme with even power allocation. In addition, results under our dynamic power allocation scheme are close to those under the power allocation with complete information.

A dynamic power allocation scheme is proposed to maximise the energy efficiency (EE) for multi-antenna transmissions when both the imperfect channel estimation and feedback are considered. The lower bounded ergodic EE is given by the temporal correlation analysis from the Kalman estimation and the differential codebook-based feedback of the Gauss–Markov channel, based on which both the powers of data and training are adaptively optimised. Simulations show that the proposed dynamic power allocation scheme outperforms the traditional power allocation schemes.

This paper presents a framework for allocating radio resources to the Access Points (APs) introducing an Access Point Controller (APC). Radio resources can be either time slots or subchannels. The APC assigns subchannels to the APs using a dynamic subchannel allocation scheme. The developed framework evaluates the dynamic subchannel allocation scheme for a downlink multicellular Orthogonal Frequency Division Multiple Access (OFDMA) system. In the considered system, each AP and the associated Mobile Terminals (MTs) are not operating on a frequency channel with fixed bandwidth, rather the channel bandwidth for each AP is dynamically adapted according to the traffic load. The subchannels assignment procedure is based on quality estimations due to the interference measurements and the current traffic load. The traffic load estimation is realized with the measurement of the utilization of the assigned radio resources. The reuse partitioning for the radio resources is done by estimating mutual Signal to Interference Ratio (SIR) of the APs. The developed dynamic subchannel allocation ensures Quality of Service (QoS), better traffic adaptability, and higher spectrum efficiency with less computational complexity.

Recently, femtocells (HeNBs) are deployed in heterogeneous network (HetNets) attributable to the addition of coverage, capacity and impeccable spectrum efficiency in private buildings/office complexes surroundings. This unplanned placement of HeNBs creates co-channel interference (CCI) in orthogonal frequency division multiple access (OFDMA). As a result service, disruption occurred at the macrocell user and femtocell user. Therefore, with always expanding interest for frequency spectrum, there is have to make available more resource allocation in order to permit more quantities of unlicensed clients to transmit their signals in the authorized groups. Cognitive-based approaches may increase the spectrum efficiency by sharing resources opportunistically from unlicensed/licensed network. However, spectrum management processes quite difficult, and a lot of chance to occur interference at the cell-edge users. Therefore, a dynamic spectrum allocation scheme is proposed for OFDMA based HetNets. The proposed scheme efficiently reduces the interferences through subchannel detection. To accomplish the CCI mitigation, the proposed scheme is functioning using the energy exposure with group creation algorithm for subchannel detection. Finally, the detected subchannel is allocated through applying power allocation algorithm. The simulation results represent that proposed scheme is able to maximize the spectrum detection and enhanced the spectral efficiency. The improved spectral efficiency recuperates the CCI situation at underlay HetNets.

IEEE 802.15.4 is a standard of Low-Rate Wireless Personal Area Network (LR-WPAN), which provides the communications in short distance, low data rate, low power consumption. In IEEE 802.15.4 a guaranteed time slot (GTS) structure is defined to support high quality of service (QoS) to time-critical traffic. However, the transmission bandwidth of GTS may be wasted because of the inefficient allocation. Moreover, the coordinator in the PAN allocates the GTSs by first-come-first-serve (FCFS) scheme, which decreases the system performance, such as delay time and throughput. In this paper, we proposed a Multi-Factor Dynamic GTS Allocation Scheme (MFDGAS) to improve the utilization of GTS bandwidth. The MFDGAS scheme determines allocation of GTS by taking the data size, delay time and the utilization of GTS time slot into consideration. The numerical results show that the proposed MFDGAS improves the performance of system throughput.