Application Of Water Cycle Algorithm For Optimal Cost Design Of Water Distribution Systems

Water distribution system (WDS) design is considered as a class of large combinatorial non-linear optimization problems having complex implicit constraints such as conservation of mass and energy equations. Due to the complexity and large feasible solution, traditional optimization techniques are not capable to tackle these kinds of problems. Recently, applications of metaheuristic algorithms, due to their efficiencies and performances, are increased dramatically. In this paper, water cycle algorithm (WCA), a recently developed population-based algorithm, coupled with hydraulic simulator, EPANET, are applied for finding the optimal cost design of WDS. The performance of the WCA is shown using well-known Balerma benchmark problem widely used in the literature. The obtained optimization results using the WCA are compared with other optimizers such as genetic algorithm, simulated annealing, and harmony search. Comparisons of obtained statistical results show the superiority of the WCA over other optimization methods in terms of convergence rate and solution quality

Adaptive Broadcasting Method Using Neighbor Type Information in Wireless Sensor Networks

Flooding is the simplest and most effective way to disseminate a packet to all nodes in a wireless sensor network (WSN). However, basic flooding makes all nodes transmit the packet at least once, resulting in the broadcast storm problem in a worst case, and in turn, network resources are severely wasted. Particularly, power is the most valuable resource of WSNs as nodes are powered by batteries, then the waste of energy by the basic flooding lessens the lifetime of WSNs. In order to solve the broadcast storm problem, this paper proposes a dynamic probabilistic flooding that utilizes the neighbor information like the numbers of child and sibling nodes. In general, the more sibling nodes there are, the higher is the probability that a broadcast packet may be sent by one of the sibling nodes. The packet is not retransmitted by itself, though. Meanwhile, if a node has many child nodes its retransmission probability should be high to achieve the high packet delivery ratio. Therefore, these two terms—the numbers of child and sibling nodes—are adopted in the proposed method in order to attain more reliable flooding. The proposed method also adopts the back-off delay scheme to avoid collisions between close neighbors. Simulation results prove that the proposed method outperforms previous flooding methods in respect of the number of duplicate packets and packet delivery ratio

Determination of the thermoelectric properties of a skutterudite-based device at practical operating temperatures by impedance spectroscopy

Skutterudite-based thermoelectric materials are promising candidates for waste heat recovery applications at intermediate temperatures (300–500 °C) owing to their high dimensionless figure of merit and power factor. Recently, several researchers have reported the high performance of skutterudite-based thermoelectric devices obtained by optimizing the crystal structure and microstructure of skutterudite materials and developing metallization layers for device fabrication. Despite extensive research efforts toward maximizing the power density and thermoelectric conversion efficiency of skutterudite-based devices, the thermoelectric properties of such devices after fabrication remain largely unknown. Here, we systematically investigated the factors that affect the thermoelectric properties of skutterudite-based devices within the range of practical operating temperatures (23–450 °C). We successfully prepared a two-couple skutterudite-based device with titanium metallization layers on both sides of the thermoelectric legs and characterized it using scanning and transmission electron microscopy and specific contact resistance measurements. Impedance spectroscopy measurements of the two-couple skutterudite-based device revealed the figure of merit of the device and enabled the extraction of three key thermoelectric parameters (Seebeck coefficient, thermal conductivity, and electrical conductivity). The impedance spectra and extracted parameters depended strongly on the measurement temperature and were mainly attributable to the thermoelectric properties of skutterudite materials. These observations demonstrate the interplay between the properties of thermoelectric materials and devices and can aid in directing future research on thermoelectric device fabrication

Impact of MAC Delay on AUV Localization: Underwater Localization Based on Hyperbolic Frequency Modulation Signal

Medium Access Control (MAC) delay which occurs between the anchor node’s transmissions is one of the error sources in underwater localization. In particular, in AUV localization, the MAC delay significantly degrades the ranging accuracy. The Cramer-Rao Low Bound (CRLB) definition theoretically proves that the MAC delay significantly degrades the localization performance. This paper proposes underwater localization combined with multiple access technology to decouple the localization performance from the MAC delay. Towards this goal, we adopt hyperbolic frequency modulation (HFM) signal that provides multiplexing based on its good property, high-temporal correlation. Owing to the multiplexing ability of the HFM signal, the anchor nodes can transmit packets without MAC delay, i.e., simultaneous transmission is possible. In addition, the simulation results show that the simultaneous transmission is not an optional communication scheme, but essential for the localization of mobile object in underwater

Network Intelligence Based on Network State Information for Connected Vehicles Utilizing Fog Computing

This paper proposes a method to take advantage of fog computing and SDN in the connected vehicle environment, where communication channels are unstable and the topology changes frequently. A controller knows the current state of the network by maintaining the most recent network topology. Of all the information collected by the controller in the mobile environment, node mobility information is particularly important. Thus, we divide nodes into three classes according to their mobility types and use their related attributes to efficiently manage the mobile connections. Our approach utilizes mobility information to reduce control message overhead by adjusting the period of beacon messages and to support efficient failure recovery. One is to recover the connection failures using only mobility information, and the other is to suggest a real-time scheduling algorithm to recover the services for the vehicles that lost connection in the case of a fog server failure. A real-time scheduling method is first described and then evaluated. The results show that our scheme is effective in the connected vehicle environment. We then demonstrate the reduction of control overhead and the connection recovery by using a network simulator. The simulation results show that control message overhead and failure recovery time are decreased by approximately 55% and 5%, respectively

Contention-Aware Adaptive Data Rate for Throughput Optimization in LoRaWAN

In Long Range Wide Area Network (LoRaWAN), the data rate of the devices can be adjusted to optimize the throughput by changing the spreading factor. However, the adaptive data rate has to be carefully utilized because the collision probability, which directly affects the throughput, is changed according to the use of spreading factors. Namely, the greater the number of devices using the same spreading factor, the greater the probability of collision, resulting in a decrease of total throughput. Nevertheless, in the current system, the only criteria to determine the data rate to be adjusted is a link quality. Therefore, contention-aware adaptive data rate should be designed for the throughput optimization. Here, the number of devices which can use a specific data rate is restricted, and accordingly the optimization problem can be regarded as constrained optimization. To find an optimal solution, we adopt the gradient projection method. By adjusting the data rate based on the retrieved set of optimal data rate, the system performance can be significantly improved. The numerical results demonstrate that the proposed method outperforms the comparisons regardless of the number of devices and is close to the theoretical upper bound of throughput

Sensor Node Activation Using Bat Algorithm for Connected Target Coverage in WSNs

This paper proposes a sensor node activation method using the nature-inspired algorithm (NIA) for the target coverage problem. The NIAs have been used to solve various optimization problems. This paper formulates the sensor target coverage problem into an object function and solves it with an NIA, specifically, the bat algorithm (BA). Although this is not the first attempt to use the BA for the coverage problem, the proposed method introduces a new concept called bat couple which consists of two bats. One bat finds sensor nodes that need to be activated for sensing, and the other finds nodes for data forwarding from active sensor nodes to a sink. Thanks to the bat couple, the proposed method can ensure connectivity from active sensor nodes to a sink through at least one communication path, focusing on the energy efficiency. In addition, unlike other methods the proposed method considers a practical feature of sensing: The detection probability of sensors decreases as the distance from the target increases. Other methods assume the binary model where the success of target detection entirely depends on whether a target is within the threshold distance from the sensor or not. Our method utilizes the probabilistic sensing model instead of the binary model. Simulation results show that the proposed method outperforms others in terms of the network lifetime

SLSMP: Time Synchronization and Localization Using Seawater Movement Pattern in Underwater Wireless Networks

Time synchronization and localization in underwater environment are challenging due to high propagation delay, time measurement error, and node mobility. Although synchronization and localization depend on each other and have the similar process, they have been usually handled separately. In this paper, we suggest time synchronization and localization based on the semiperiodic property of seawater movement, called SLSMP. Firstly, we analyze error factors in time synchronization and localization and then propose a method to handle those errors. For more accurate synchronization, SLSMP controls the transmission instant by exploiting the pattern of seawater movement and node deployment. Then SLSMP progressively decreases the localization errors by applying the Kalman filter or averaging filter. Finally, INS (inertial navigation system) is adopted to relieve localization error caused by node mobility and error propagation problem. The simulation results show that SLSMP reduces time synchronization error by 2.5 ms and 0.56 ms compared with TSHL and MU-Sync, respectively. Also localization error is lessened by 44.73% compared with the single multilateration

A Security Framework for Cluster-Based Wireless Sensor Networks against the Selfishness Problem

Over the last few decades, Cluster-Based Wireless Sensor Networks (CBWSNs) have played a crucial role in handling various challenges (load balancing, routing, network lifetime, etc.) of large scale Wireless Sensor Networks (WSNs). However, the security becomes a big problem for CBWSNs, especially when nodes in the cluster selfishly behave, e.g., not forwarding other nodes’ data, to save their limited resources. This may make the cluster obsolete, even destroying the network. Thus, a way to guarantee the secure and consistent clusters is needed for proper working of CBWSNs. We showed that the selfishness attack, i.e., passive attack or insider attack, in CBWSNs can cause severe performance disaster, when particularly a cluster head node becomes selfish. In order to prevent this situation, this paper proposes a security framework that involves a novel clustering technique as well as a reputation system at nodes for controlling selfishness, making them cooperative and honest. The novelty of the clustering comes from the existence of inspector node (IN) to monitor the cluster head (CH) and its special working style. The experimental results showed that the proposed security framework can control the selfishness and improve the security of the clusters

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home