Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method

A Movement of Mobile Sink in Wireless Sensor Network to Conserve Energy

Energy is the major constraint in wireless sensor network. In wireless sensor network with static mobile collector (SNSMC),static nodes located near to sink consume more energy, since the nodes relay the data collected by sensor nodes far away from the sink. The battery drained in short time. This problem is resolved by the MMC-WSN method. While simplifying the routing process, proposing an energy-efficient routing technique based on cluster based method for mobile sink is preferred. First part ,the selection of cluster head (CH) in cluster based method made periodically according to their residual energy and in second part the mobile sink moves across the sensing field and directly collects data from cluster heads and returns to back to initial site in a specific sequence based on spanning graphs. The spanning graph includes the shortest search path for the MS. Finally, a tour-planning algorithm is used on the basis of the spanning graph. An energy efficient routing technique (EFR) in WSNs among obstacles uses the shortest route. In this way, the mobile sink retrieves all detected knowledge among a given time and sends to base station which reduces the packet delay and energy-consumption and WSNs

Data collection algorithm for wireless sensor networks using collaborative mobile elements

The simplest approach to reduce network latency for data gathering in wireless sensor networks (WSN) is to use multiple mobile elements rather than a single mobile sink. However, the most challneging issues faced this approach are firstly the high network cost as a result of using large number of mobile elements. Secondly, it suffers from the difficulty of network partitioning to achieve an efficient load balancing among these mobile elements. In this study, a collaborative data collection algorithm (CDCA) is developed. Simulation results presented in this paper demonstrated that with this algorithm the latency is significantly reduced at small number of mobile elements. Furthermore, the performance of CDCA algorithm is compared with the Area Splitting Algorithm (ASA). Consequently, the CDCA showed superior performance in terms of network latency, load balancing, and the required number of mobile elements

Sencar Based Load Balanced Clustering With Mobile Data Gathering In Wireless Sensor Networks

The wireless sensor networks consist of static sensors, which can be deployed in a wide environment for monitoring applications. While transmitting the data from source to static sink, the amount of energy consumption of the sensor node is high. This results in reduced lifetime of the network. Some of the WSN architectures have been proposed based on Mobile Elements such as three-layer framework is for mobile data collection, which includes the sensor layer, cluster head layer, and mobile collector layer (called SenCar layer). This framework employs distributed load balanced clustering and dual data uploading, it is referred to as LBC-DDU.In the sensor layer a distributed load balanced clustering algorithm is used for sensors to self-organize themselves into clusters. The cluster head layer use inter-cluster transmission range it is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving in the inter-cluster communications. Through this transmissions cluster head information is send to the SenCar for its moving trajectory planning.This is done by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. Then the results show each cluster has at most two cluster heads. LBC-DDU achieves higher energy saving per node and energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sinks

Mobility in wireless sensor networks : advantages, limitations and effects

The primary aim of this thesis is to study the benefits and limitations of using a mobile base station for data gathering in wireless sensor networks. The case of a single mobile base station and mobile relays are considered. A cluster-based algorithm to determine the trajectory of a mobile base station for data gathering within a specified delay time is presented. The proposed algorithm aims for an equal number of sensors in each cluster in order to achieve load balance among the cluster heads. It is shown that there is a tradeoff between data-gathering delay and balancing energy consumption among sensor nodes. An analytical solution to the problem is provided in terms of the speed of the mobile base station. Simulation is performed to evaluate the performance of the proposed algorithm against the static case and to evaluate the distribution of energy consumption among the cluster heads. It is demonstrated that the use of clustering with a mobile base station can improve the network lifetime and that the proposed algorithm balances energy consumption among cluster heads. The effect of the base station velocity on the number of packet losses is studied and highlights the limitation of using a mobile base station for a large-scale network. We consider a scenario where a number of mobile relays roam through the sensing field and have limited energy resources that cannot reach each other directly. A routing scheme based on the multipath protocol is proposed, and explores how the number of paths and spread of neighbour nodes used by the mobile relays to communicate affects the network overhead. We introduce the idea of allowing the source mobile relay to cache multiple routes to the destination through its neighbour nodes in order to provide redundant paths to destination. An analytical model of network overhead is developed and verified by simulation. It is shown that the desirable number of routes is dependent on the velocity of the mobile relays. In most cases the network overhead is minimized when the source mobile relay caches six paths via appropriately distributed neighbours at the destination. A new technique for estimating routing-path hop count is also proposed. An analytical model is provided to estimate the hop count between source-destination pairs in a wireless network with an arbitrary node degree when the network nodes are uniformly distributed in the sensing field. The proposed model is a significant improvement over existing models, which do not correctly address the low-node density situation

Restricting Barrier and Finding the Shortest Path in Wireless Sensor Network Using Mobile Sink

Wireless Sensor Network (WSN) is a collection of spatially deployed in wireless sensors. In general, sensing field could contain various barriers which cause loss of information transferring towards the destination. As a remedy, this proposed work presents an energy-efficient routing mechanism based on cluster in mobile sink. The scope of this work is to provide a mobile sink in a single mobile node which begins data-gathering from starting stage, then immediately collects facts from cluster heads in single-hop range and subsequently, it returns to the starting stage. During the movement of the mobile sink if the barrier exists in the sensing field it can be detected using Spanning graph and Grid based techniques. The possible locations for the mobile sink movement can be reduced easily by Spanning graph. At last, Barrier avoidance-shortest route was established for mobile sink using Dijkstra algorithm. The Distributed location information is collected using a Timer Bloom Filter Aggregation (TBFA) scheme. In the TBFA scheme, the location information of Mobile node (MNs) is maintained by Bloom filters by each Mobile agent (MA). Since the propagation of the whole Bloom filter for every Mobile node (MN) movement leads to high signaling overhead, each Mobile agent (MA) only propagates changed indexes in the Bloom filter when a pre-defined timer expires. To verify the performance of the TBFA scheme, an analytical model is developed on the signaling overhead and the latency and devise an algorithm to select an appropriate timer value. Extensive simulation and Network Simulator 2(NS2) results are given to show the accuracy of analytical models and effectiveness of the proposed method

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks

An effective data-collection scheme with AUV path planning in underwater wireless sensor networks

Data collection in underwater wireless sensor networks (UWSNs) using autonomous underwater vehicles (AUVs) is a more robust solution than traditional approaches, instead of transmitting data from each node to a destination node. However, the design of delay-aware and energy-efficient path planning for AUVs is one of the most crucial problems in collecting data for UWSNs. To reduce network delay and increase network lifetime, we proposed a novel reliable AUV-based data-collection routing protocol for UWSNs. The proposed protocol employs a route planning mechanism to collect data using AUVs. The sink node directs AUVs for data collection from sensor nodes to reduce energy consumption. First, sensor nodes are organized into clusters for better scalability, and then, these clusters are arranged into groups to assign an AUV to each group. Second, the traveling path for each AUV is crafted based on the Markov decision process (MDP) for the reliable collection of data. The simulation results affirm the effectiveness and efficiency of the proposed technique in terms of throughput, energy efficiency, delay, and reliability. © 2022 Wahab Khan et al