Neural network and genetic algorithm techniques for energy efficient relay node placement in smart grid

Smart grid (SG) is an intelligent combination of computer science and electricity system whose main characteristics are measurement and real-time monitoring for utility and consumer behavior. SG is made of three main parts: Home Area Network (HAN), Field Area Network (FAN) and Wide Area Network (WAN). There are several techniques used for monitoring SG such as fiber optic but very costly and difficult to maintain. One of the ways to solve the monitoring problem is use of Wireless Sensor Network (WSN). WSN is widely researched because of its easy deployment, low maintenance requirements, small hardware and low costs. However, SG is a harsh environment with high level of magnetic field and background noise and deploying WSN in this area is challenging since it has a direct effect on WSN link quality. An optimal relay node placement which has not yet worked in a smart grid can improve the link quality significantly. To solve the link quality problem and achieve optimum relay node placement, network life-time must be calculated because a longer life-time indicates better relay placement. To calculate this life-time, it is necessary to estimate packet reception rate (PRR). In this research, to achieve optimal relay node placement, firstly, a mathematical formula to measure link quality of the network in smart grid environment is proposed. Secondly, an algorithm based on neural network to estimate the network life-time has been developed. Thirdly, an algorithm based on genetic algorithm for efcient positioning of relay nodes under different conditions to increase the life-time of neural network has also been developed. Results from simulation showed that life-time prediction of neural network has a 91% accuracy. In addition, there was an 85% improvement of life-time compared to binary integer linear programming and weight binary integer linear programming. The research has shown that relay node placement based on the developed genetic algorithms have increased the network life-time, addressed the link quality problem and achieved optimum relay node placement

Connectivity, Coverage and Placement in Wireless Sensor Networks

Wireless communication between sensors allows the formation of flexible sensor networks, which can be deployed rapidly over wide or inaccessible areas. However, the need to gather data from all sensors in the network imposes constraints on the distances between sensors. This survey describes the state of the art in techniques for determining the minimum density and optimal locations of relay nodes and ordinary sensors to ensure connectivity, subject to various degrees of uncertainty in the locations of the nodes

Improving the lifetime of two-tiered sensor networks using genetic algorithm

Wireless sensor networks have been envisioned to have a wide range of applications which consist of many inexpensive and low-powered wireless nodes which are used to sense, gather, and transmit the data towards the base station. In Two-Tiered wireless sensor networks, nodes are grouped into clusters, with a minimum of one cluster-head to distribute the work load among the member nodes. In the recent years, higher-powered relay nodes have been proposed to act as cluster heads and these relay nodes form a network among themselves in order to improve the lifetime of the sensor networks. Since the nodes are generally energy constrained, efficient management of the network data communication scheme can maximize the lifetime of the networks. A Genetic Algorithm is the technique for randomized search and optimization which is based on Darwin\u27s Principal of Natural Selection. In this paper, we have proposed a Genetic Algorithm based solution for scheduling the data gathering of relay nodes that can significantly extend the lifetime of the relay node network. We have simulated our method on 15 different sizes of networks and measured the lifetime of the network as the number of rounds, until the first relay node runs out of battery power. For smaller networks, where the global optimum can be determined, our genetic algorithm based approach is always able to find the optimal solution with a lesser program run-time. For larger networks, we have compared our approach with traditional routing schemes and shown that our method leads to significant improvements

Energy efficient in cluster head and relay node selection for wireless sensor networks

Wireless Sensor Networks (WSNs) are defined as networks of nodes that work in a cooperative way to sense and control the surrounding environment. However, nodes contain limited energy which is the key limiting factor of the sensor network operation. In WSN architecture, the nodes are typically grouped into clusters where one node from each cluster is selected as the Cluster Head (CH) and relays utilisation to minimise energy consumption. Currently, the selection of CH based on a different combination of input variables. Example of these variables includes residual energy, communication cost, node density, mobility, cluster size and many others. Improper selection of sensor node (i.e. weak signal strength) as CH can cause an increase in energy consumption. Additionally, a direct transmission in dual-hop communication between sensor nodes (e.g. CH) with the base station (BS) uses high energy consumption. A proper selection of the relay node can assist in communication while minimising energy consumption. Therefore, the research aim is to prolong the network lifetime (i.e. reduce energy consumption) by improving the selection of CHs and relay nodes through a new combination of input variables and distance threshold approach. In CH selection, the Received Signal Strength Indicator (RSSI) scheme, residual energy, and centrality variable were proposed. Fuzzy logic was utilized in selecting the appropriate CHs based on these variables in the MATLAB. In relay node selection, the selection is based on the distance threshold according to the nearest distance with the BS. The selection of the optimal number of relay nodes is performed using K-Optimal and K-Means techniques. This ensures that all CHs are connected to at least one corresponding relay node (i.e. a 2-tier network) to execute the routing process and send the data to BS. To evaluate the proposal, the performance of Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) was compared based on 100, 200, and 800 nodes with 1 J and random energy. The simulation results showed that our proposed approach, refer to as Energy Efficient Cluster Heads and Relay Nodes (EECR) selection approach, extended the network lifetime of the wireless sensor network by 43% and 33% longer than SEP and MAP, respectively. This thesis concluded that with effective combinations of variables for CHs and relay nodes selection in static environment for data routing, EECR can effectively improve the energy efficiency of WSNs

Ant Colony Optimization for Jointly Solving Relay Node Placement and Trajectory Calculation in Hierarchical Wireless Sensor Networks

Given the locations of the Sensor Nodes in a Wireless Sensor Networks (WSN), finding the minimum number of Relays required and their locations such that each sensor is covered by at least one relay is called the Relay Node Placement (RNP) problem. Given the locations of the relays, finding an optimized trajectory for the Mobile Data Collector (MDC) is another important design problem of the WSN domain. Previous researchers have shown that jointly solving different design problems in the WSN domain often leads to better overall results. In recent years, Ant Colony Optimization (ACO) have emerged as an effective tool for solving complex optimization problems. An ACO based approach for solving the joint problem of Relay Node Placement & Trajectory calculation(RNPT) is presented in this thesis. We also present a deterministic, and a Continuous Ant Colony Optimization ([Special characters omitted.] ACOR ) approach for refining the trajectory produced by the ACO approach

Planning the deployment of fault-tolerant wireless sensor networks

Since Wireless Sensor Networks (WSNs) are subject to failures, fault-tolerance becomes an important requirement for many WSN applications. Fault-tolerance can be enabled in different areas of WSN design and operation, including the Medium Access Control (MAC) layer and the initial topology design. To be robust to failures, a MAC protocol must be able to adapt to traffic fluctuations and topology dynamics. We design ER-MAC that can switch from energy-efficient operation in normal monitoring to reliable and fast delivery for emergency monitoring, and vice versa. It also can prioritise high priority packets and guarantee fair packet deliveries from all sensor nodes. Topology design supports fault-tolerance by ensuring that there are alternative acceptable routes to data sinks when failures occur. We provide solutions for four topology planning problems: Additional Relay Placement (ARP), Additional Backup Placement (ABP), Multiple Sink Placement (MSP), and Multiple Sink and Relay Placement (MSRP). Our solutions use a local search technique based on Greedy Randomized Adaptive Search Procedures (GRASP). GRASP-ARP deploys relays for (k,l)-sink-connectivity, where each sensor node must have k vertex-disjoint paths of length ≤ l. To count how many disjoint paths a node has, we propose Counting-Paths. GRASP-ABP deploys fewer relays than GRASP-ARP by focusing only on the most important nodes – those whose failure has the worst effect. To identify such nodes, we define Length-constrained Connectivity and Rerouting Centrality (l-CRC). Greedy-MSP and GRASP-MSP place minimal cost sinks to ensure that each sensor node in the network is double-covered, i.e. has two length-bounded paths to two sinks. Greedy-MSRP and GRASP-MSRP deploy sinks and relays with minimal cost to make the network double-covered and non-critical, i.e. all sensor nodes must have length-bounded alternative paths to sinks when an arbitrary sensor node fails. We then evaluate the fault-tolerance of each topology in data gathering simulations using ER-MAC