Lifetime Of Wireless Networks Research Articles

Optimization and Analysis of Wireless Networks Lifetime using Soft Computing for Industrial Applications

Recently, wireless networks are applied in various engineering and industrial applications. One of the critical problems in wireless network system optimization in intelligent applications is obtaining an adequate energy fairness level. This issue can be resolved by applying effective cluster-based routing optimization with multi-hop routing. Hence a new network structure is developed that is derived from energy consumption architecture by applying soft computing strategies such as evolutionary operators in determining the exact clusters for optimizing energy consumption. The new effective evolutionary operators are tested in the optimization of a lifetime. The proposed method is simulated for different values of the routing factor, α, for different types of networks. The energy levels range from 0.4 to 0.8, achieving good results for nearly 2500 rounds. The proposed strategy optimizes the clusters, and its head is selected reliably. The optimization of cluster head choice has been done based on the base station distance, the energy of the node, and the node's energy efficiency. The reliability of the long-distance nodes is increased during the data transmission by modifying the size of the area of the candidate set of nodes in contrast the near-distance node's energy consumption is reduced. For the energy levels that range from 0.4 to 0.8, the higher network throughput is obtained at the same time network lifetime is optimized compared to other well-known approaches. The proposed model is expected for different industrial wireless network applications to optimize the systems during the long-run simulation and to achieve high reliability and sustainability.

Enhancement of Energy Harvesting Efficiency in Mobile Wireless Sensor Networks

Mobile wireless sensor networks suffer from the restricted availability of energy supplies. In this research work, a proposed method for extending the lifetime of energy-constrained mobile wireless sensor networks (MWSNs) is presented. This method is based on the fact that RF signal carries both information and energy at the same time. Hence, by increasing the efficiency of energy harvesting from radio frequency (RF) signals, the lifetime of the wireless network can be significantly extended. The Simultaneous Wireless Information and Power Transfer (SWIPT) technique enables harvesting of energy by relay nodes which in turn can be used for wireless data transmission. To enhance the lifetime of the mobile wireless network, the transmitted RF energy can be recycled at the receiver side. On the other hand, a balance between energy harvesting and wireless data transmission is required in to maximize the overall efficiency of the system. Particle Swarm Optimization (PSO) is employed to obtain the optimum resource allocation policy which maximizes the system energy efficiency. A cost function is framed for this purpose and PSO attains the maximum energy efficiency by improving the solution of the cost function at each iteration with respect to given constraints.

Optimization of Energy Harvesting in Mobile Wireless Sensor Networks

The restricted availability of energy supplies is one of the major challenges in designing mobile wireless sensor networks. In this research work, a proposed method for extending the lifetime of energy-constrained mobile wireless sensor networks (MWSNs) is presented. This method is based on the fact that RF signal carries both information and energy at the same time. Hence, by increasing the efficiency of energy harvesting from radio frequency (RF) signals, the lifetime of the wireless network can be significantly extended. The Simultaneous Wireless Information and Power Transfer (SWIPT) technique enables harvesting of energy by relay nodes which in turn can be used for wireless data transmission. To enhance the lifetime of the mobile wireless network, the transmitted RF energy can be recycled at the receiver side. On the other hand, a balance between energy harvesting and wireless data transmission is required in to maximize the overall efficiency of the system. Particle Swarm Optimization (PSO) is employed to obtain the optimum resource allocation policy which maximizes the system energy efficiency. A cost function is framed for this purpose and PSO attains the maximum energy efficiency by improving the solution of the cost function at each iteration with respect to given constraints.

Investigation on energy harvesting enabled device-to-device networks in presence of co-channel interference

Energy harvesting from ambient radio-frequency (RF) sources has been a novel approach for extending the lifetime of wireless networks. In this paper, a cooperative device-to-device (D2D) system with the aid of energy-constrained relay is considered. The relays are assumed to be able to harvest energy from information signal and co-channel interference (CCI) signals broadcasted by nearby traditional cellular users and forward the source’s signal to its desired destination (D2D user) utilizing amplify-andforward (AF) relaying protocol. Time switching protocol (TSR) and power splitting protocol (PSR) are proposed to assist energy harvesting and information processing at the relay. The proposed approaches are applied in a model with three nodes including the source (D2D user), the relay and the destination (D2D user), the system throughput is investigated in terms of the ergodic capacity and the outage capacity, where the analytical results are obtained approximately. Our numerical results verify the our derivations, and also points out the impact of CCI on system performance. Finally, this investigation provide fundamental design guidelines for selecting hardware of energy harvesting circuits that satisfies the requirements of a practical cooperative D2D system.

Optimal SWIPT in RIS-Aided MIMO Networks

The use of simultaneous wireless information and power transfer (SWIPT) is a key enabler of achieving convenience and prolonging the energy supply lifetime of wireless networks. To address the low efficiencies of far-field power transfer, a reconfigurable intelligent surface (RIS) is adopted to enhance the energy harvesting (EH) performance, which can construct a favorable wireless propagation environment. In this paper, we consider an RIS-assisted SWIPT system with wireless transfer from the access point (AP) to multiple-antenna receivers, which include information receivers (IRs) and energy receivers (ERs). First, we formulate the problem of maximizing the minimum rates of the IRs as a nonconvex constrained optimization problem. For ideal and nonideal channels, we propose two different solutions. Second, we simplify the objective function and decompose the problem into several subproblems by using sorting and iterative optimization algorithms. Moreover, under optimal boundary and Karush-Kuhn-Tucker (KKT) conditions, we successfully solve this problem. The simulation results illustrate a promising approach for wireless communication by comparing ideal RIS and unsatisfactory situations with no RIS cases under various conditions.

Coyote Optimization Based on a Fuzzy Logic Algorithm for Energy-Efficiency in Wireless Sensor Networks

Internet of Things (IoT) is an important technique in the modern wireless telecommunications field. It is based on a collection of sensor nodes connected through wireless sensor networks (WSNs). The lifetime of this network is affected by the battery power of the connected sensor nodes. Network clustering techniques are used to improve energy consumption and extend the lifetime of the WSN. These techniques divide the sensor nodes into clusters and every cluster has a unique cluster head (CH) node. Recently, clustering-based metaheuristic techniques are used to solve this problem and find the optimal CH nodes under certain considerations such as less energy consumption and high reliability. This paper proposes a new clustering scheme for heterogeneous WSN using Coyote Optimization based on a Fuzzy Logic (COFL) algorithm. It uses the coyote optimization algorithm (COA) in conjunction with fuzzy logic (FL) system to reinforce and balance the clustering process for increasing the wireless network lifetime and reducing energy consumption. FL based clustering is adapted to determine a tentative set of CHs. The output of the FL is added as a solution within the initial solutions of the COA. Furthermore, a new fitness function has been adapted to minimize the total intra-cluster distance between each CH node and its cluster members and minimize the inter-cluster distance between the CHs nodes and the base station. An extensive simulation with three different scenarios is performed. The performance of the proposed COFL algorithm is compared with the well-known algorithms; namely low-energy adaptive clustering hierarchy protocol (LEACH) and stable election protocol (SEP) as traditional protocols and also coyote optimization algorithm (COA), grey wolf optimization (GWO), and particle swarm optimization (PSO). The COFL algorithm outperforms other algorithms in terms of alive node analysis, energy consumption, throughput, and central tendency measurements for alive nodes and normalized energy.

Dynamic Wireless Energy Harvesting and Optimal Distribution in Multipair DF Relay Network with Nonlinear Energy Conversion Model

Wireless energy harvesting has emerged as an efficient solution to prolong the lifetime of wireless networks composed of energy‐constrained nodes. In this paper, we consider a multipoint‐to‐multipoint relay network, where multiple source nodes communicate with their respective destination nodes via intermediate energy‐constrained decode‐and‐forward (DF) relay. The performance of two different transmission modes, namely, delay tolerant and delay nontolerant, is studied. Based on power‐splitting relaying protocol (PSR), optimal energy harvesting and distribution schemes for both transmission modes are provided. In addition, for more realistic and practical analysis, we consider a nonlinear energy conversion model for energy harvesting at the relay node. Our numerical results provide useful insights into different system parameters of a nonlinear energy harvesting‐based multipair DF relay network.

On the Optimization Model for Multi-Hop Information Transmission and Energy Transfer in TDMA-Based Wireless Sensor Networks

Wireless energy transfer (WET) has been considered a promising technique for prolonging the lifetime of wireless networks. In this letter, the joint optimization problem of multi-hop information transmission and energy transfer in TDMA-based multi-hop wireless sensor networks is modeled. Moreover, a more realistic energy consumption model is proposed, which takes the energy consumption of compression, communication, and wireless energy transfer into account. A nonlinear programming problem is formulated to achieve the maximal source rate utility. Finally, through the numerical results, the impact of system parameters on rate utility is investigated, and the role of WET in improving network performance is verified.

RF-Based Energy Harvesting in Decode-and-Forward Relaying Systems: Ergodic and Outage Capacities

Radio-frequency energy harvesting constitutes an effective way to prolong the lifetime of wireless networks, wean communication devices off the battery and power line, benefit the energy saving and lower the carbon footprint of wireless communications. In this paper, an interference aided energy harvesting scheme is proposed for cooperative relaying systems, where energy-constrained relays harvest energy from the received information signal and co-channel interference signals, and then use that harvested energy to forward the correctly decoded signal to the destination. The time-switching scheme (TS), in which the receiver switches between decoding information and harvesting energy, as well as the power-splitting scheme (PS), where a portion of the received power is used for energy harvesting and the remaining power is utilized for information processing, are adopted separately. Applying the proposed energy harvesting approach to a decode-and-forward relaying system with the three-terminal model, the analytical expressions of the ergodic capacity and the outage capacity are derived, and the corresponding achievable throughputs are determined. Comparative results are provided and show that PS is superior to TS at high signal-to-noise ratio (SNR) in terms of throughput, while at low SNR, TS outperforms PS. Furthermore, considering different interference power distributions with equal aggregate interference power at the relay, the corresponding system capacity relationship, i.e., the ordering of capacities, is obtained.

Analysis of dynamic path loss based on the RSSI model for rupture location analysis in underground wireless sensor networks and its implications for Earthquake Early Warning System (EEWS)

Sensors deployed in underground tunnels found that radio frequency signals suffer significant signal strength attenuation which can result in considerable variation of link quality on the receiving end. This study analyzes the received signal strength index (RSSI) based on the development of a theoretical wireless sensor model for data collection by enabling sensors to determine the location from which each data packet is obtained. To improve positioning accuracy, the complex radio wave propagation environment requires the use of a voronoi cell to minimize signal attenuation. A relatively simple calculation is used to predict the intensity and perception range of the received wireless signals to measure the extent of signal reduction in the attenuating rock medium. Simulation results show that RSSI-based localization and wireless network lifetime and throughput measurements are more accurate when the node concept is applied to the self-locating rupture zones than the maximum likelihood estimation method. The proposed minimum energy relay routing technique based on beacon node chain deployment is found to help correct localization errors resulting from interference caused by the underground tunnel environment. The extent of localization and power of the sensor nodes are determined based on the beacon node chain deployment of tunnel wireless sensor networks. The algorithm accounts for the distance and the corresponding RSSI between adjacent beacon nodes to calculate the actual path loss parameter in the tunnel. The proposed model can serve as the theoretical basis for locating ruptures in underground wireless sensor network nodes, thus maximizing the monitoring range of large scale tectonic environments while minimizing equipment cost. We recommend that this model can be field tested through a series of experiments by researchers and engineers working in seismology, telecommunication, and information technology.

Optimizing Cost and Throuhput Efficiency with Security in Wireless Ad HOC Networks Using Leach

Wireless ad hoc network (WANET) is a decentralized type of the wireless network. There are various challenges that are faced in the ad hoc environment. Wireless ad hoc networking is the enabling technology for paramount civilian and military applications requiring easy and quick (and often unmanned and cheap) network deployments. The security of communication in ad hoc wireless network is very important especially in military applications. The wireless nature of communication and any lack of network security infrastructure raise several security problems. Asymptotic notations solve the problem of large wireless networks. The main objective of the system is performance degradation of the secure network. In this paper mainly considering, order to reduce delay and increase throughput. The network performance can be calculated by using probability (p<1). Here we implemented LEACH algorithm for an efficient energy saving and thus increases the life time of wireless network. The simulation results show the presented algorithm is more efficient energy saving and fewer packets are damaged; thus provides secure wireless network.

Energy-aware weighted graph based dynamic topology control algorithm

In this paper a new energy-aware weighted dynamic topology control (WDTC) algorithm is proposed to extend the lifetime of wireless network and balance the nodes’ energy consumption. The idea is that each node builds its local minimum spanning tree (MST) based on the energy-aware weighted graph and the network topology is adjusted accordingly. It was proved theoretically that the topology under WDTC algorithm could preserve the network connectivity and a sufficient condition for the degree of no more than 6 was also given. Simulation shows that WDTC algorithm can effectively prolong the network lifetime and has good topological features.

Prolonging network lifetime with multi-domain cooperation strategies in wireless sensor networks

In wireless sensor networks nodes forward the data they acquire from the environment towards the base station by relaying through multiple intermediate sensor nodes used as relays. If multiple sensor networks are deployed in close proximity then they can help each others’ data forwarding so that all parties involved in such cooperation benefit from this collaborative effort (i.e., energy consumption for packet relaying reduces and the lifetime of each network is prolonged). There are many parameters that affect the impact of cooperation on sensor network lifetime such as number of domains, node density, network area, propagation environment, network topology, and base station deployment. In this paper we investigate cooperation strategies for prolonging sensor network lifetime in multi-domain wireless sensor networks through a linear programming framework. While our model is detailed enough to capture the essence of the multi-domain cooperation we intentionally avoid implementation specific details. Hence, we use our framework to determine almost achievable performance benchmarks in idealized yet practical settings. Our results show that under certain conditions (sparse network deployment and harsh propagation environment) multi-domain cooperation can extend the wireless network lifetime more than an order of magnitude when compared to non-cooperating domains of wireless sensor networks.

Optimal Relay Location for Resource-limited Energy-efficient Wireless Communication

In the design of wireless networks, techniques for improving energy efficiency and extending network lifetime have great importance, particularly for defense and civil/rescue applications where resupplying transmitters with new batteries is not feasible. In this paper we study a method for improving the lifetime of wireless networks by minimizing the length of the longest edge in the interconnecting tree by deploying additional relay nodes at specific locations. This optimization problem, known as the Bottleneck Steiner Tree Problem (BSTP), asks to find a Steiner tree for n terminals with at most k Steiner points such that the length of the longest edge in the tree is minimized. We present a ratio- \(\sqrt{3}+\epsilon\) polynomial time approximation algorithm for BSTP, where \(\epsilon\) is an arbitrary positive number.