Mobile Data Gathering with Multiuser MIMO Technique in Wireless Sensor Networks

The advantages of deploying mobile sinks (MSs) in wireless sensor networks (WSNs) have attracted tremendous attention recently, because mobility of a sink provides an efficient method to improve data gathering performance in WSNs. However, in such kind of WSN, the efficiency of data dissemination is obviously affected by the trajectory of the MS. Therefore, optimal moving schemes of the MS have been studied in many researches. Nevertheless, in many cases, the trajectory of the MS is not able to change ideally as previous studies assumed but depending on the environmental conditions. In this paper, we focus on the efficiency of data gathering in WSN with a MS whose trajectory cannot change ideally and propose a new data diffusion scheme - implementing a new Pre-Diffusion Algorithm (PDA) in a hierarchical WSN aimed at improving data gathering efficiency. This hierarchical WSN is composed of several clusters, and datatransmission is done by higher powered cluster head (CH) node. Our scheme predetermines the cluster area where the MS is going to arrive in most probability in next period of time. MS fulfills data converging in the cluster head (CH)whose distance to the MS may the smallest compared with other CHs when data gathering starts. Thus more data can be collected with less trajectory length of MS. Simulation results show that our proposed scheme improves data gathering efficiency as compared to conventional scheme.

Efficient path planning and data gathering protocols for the wireless sensor network

This study discusses about various approaches available for achieving energy-efficient data collection in wireless sensor networks. In wireless sensor networks, movable sink (MULE-Mobile Ubiquitous LAN Extensions) moving at a constant pace over a constrained path may increase energy efficiency. The mobile sink may randomly visit each sensor node and gather data in a short amount of time. These methods provide several hurdles in terms of data collection and power usage. To overcome this issue, this study has created the Maximum Amount Shortest Path (MASP) data gathering approach, which boosts performance. The MASP system is implemented using a twophase communication mechanism. Simulations in NS2 are used to test the suggested algorithms and protocols. The wireless sensor network has gotten a lot of interest in the academic community in the past several years. Many factors, such as resource limits on energy and power needed for computing, have been extensively researched. Because sensor nodes are battery-operated devices, the fundamental challenge in Wireless Sensor Networks i s reducing the amount of energy spent by the nodes so that the network’s lifetime may be extended. WSN also has privacy problems. Taking these two factors into consideration, this study examines the energy consumption of sensor node components and highlight the key possibilities for energy conservation in WSN in this work. Different battery management and security strategies that are currently available in WSN are also analyzed. Finally, this research study has come up with some energy saving ideas for future WSN research.

SummaryBecause of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device. Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT‐based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy‐efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research describe an Energy Aware Clustering and Multihop Routing Protocol with mobile sink (EACMRP‐MS) technique for IoT supported WSN. The EACMRP‐MS technique's purpose is to efficiently reduce the energy consumption of IoT sensor nodes, consequently increasing the network efficiency of the IoT system. The suggested EACMRP‐MS technique initially relies on the Tunicate Swarm Algorithm (TSA) for cluster head (CH) selection and cluster assembly, as well as the TSA. Furthermore, the type‐II fuzzy logic (T2FL) technique is used for the optimal selection of multi‐hop routes, with multiple input parameters being used to achieve this. Finally, a mobile sink with route adjustment scheme is presented to further increase the energy efficiency of the IoT system. This scheme allows for the adjustment of routes based on the trajectory of the mobile sink, which further improves the energy efficiency of the system. Using a detailed experimental analysis and simulation findings, it was discovered that the EACMRP‐MS technique outperformed the most recent state of the art methods in terms of a variety of evaluation metrics, indicating that it is a promising alternative.

Wireless sensor networks (WSNs) are formed using a cluster of sensor nodes (SNs), deployed randomly to perform sensing operations in an area under observation. Due to the unavailability of an external power source, the energy efficiency considered as one of the critical issues in WSNs. Selection of a sensor node (SN) from a wireless sensor network (WSN) cluster to serve as an aggregator or cluster head (CH), considered as an efficient method to increase the lifetime of wireless sensor network (WSN). In this paper, we have proposed an energy efficient CH selection scheme for WSN, to enhance the lifetime and average residual energy of a single WSN cluster. The proposed strategy nominates a group SNs as CHs, based on their channel condition with the base station (BS) and their residual energy. The proposed algorithm is helpful in solving the problem of unbalanced energy consumption in WSNs. Furthermore, the mechanism of using mobile sink during the hand-off stage helps to overcome the delay in data transmission. Moreover, the incorporation of energy harvesting significantly increases the lifetime of WSN. In comparison to a state-of-art technique available in the literature, our scheme shows a 33% increase in lifetime and presents a steady decrease in residual energy for the same rounds of data transmission.

Recently, several studies have considered the use of mobile sink (MS) for data gathering in wireless sensor networks. As, it can enhance lifetime of the sensor network by distributing load among the sensors. In some delay-critical applications, a mobile sink is allowed to move along a predefined path. However, due to the predefined path and relatively slower speed of mobile sink, data gathering from sensors may delayed. Thus, time-sensitive data collection in a constrained path environment using mobile sink has increased attention in the research community. Our proposed paper focuses on finding a sub-path on a given path in the network such that the mobile sink can collect maximum data from the network within given time. We refer it as maximum data gathering by optimal sub-path finding problem (MDGOSP). We develop a deterministic algorithm to solve it. Furthermore, performance of the proposed algorithm is validated through simulation experiments using MATLAB.

Wireless sensor networks (WSNs) are considered the backbone of the Internet of Things (IoT), which enables sensor nodes (SNs) to achieve applications similarly to human intelligence. However, integrating a WSN with the IoT is challenging and causes issues that require careful exploration. Prolonging the lifetime of a network through appropriately utilising energy consumption is among the essential challenges due to the limited resources of SNs. Thus, recent research has examined mobile sinks (MSs), which have been introduced to improve the overall efficiency of WSNs. MSs bear the burden of data collection instead of consuming energy at the routeing by SNs. In a network, some areas generate more data through SNs that contain frequent, urgent messages. These messages carry sensitive data that must be delivered immediately to user applications. Collecting such messages via MSs, especially on a large scale, increases delays, which are not tolerable in some real applications. This issue has not been studied much. Thus, the present study utilises the advantages of the priority parameter to concentrate on these areas and proposes a new model named ‘energy efficient path planning of MS-based area priority’ (EEPP-BAP). This method involves non-urgent and urgent messages. It is comprised of four procedures. Initially, after SNs are distributed randomly in a wide monitoring field, the monitoring field is partitioned into equal zones according to priority, either differently or equally. Next is clustering based on the cluster head (CH) selected to perform the particle swarm optimisation algorithm (PSO). Then, the MS moves first to the zones with higher priority and less distance to perform the brain storm optimisation algorithm. Finally, for urgent messages from the other zones at which the MS continues, the proposed approach establishes a routeing technique using multi-hop communication based on the MS position and using PSO. The proposed solution is aimed at delivering urgent messages to MSs free of latency and with minimal packet loss. The simulation results proved that the EEPP-BAP method can improve network performance compared with other models based on different parameters that have been used to construct the controlled movement of MSs in large-scale environments involving urgent messages. The proposed method increased the average lifetime of SNs to 206.6% on average, reduced the average end-to-end delay to 7.1%, and increased the average packet delivery ratio to 36.9%.

The issue “Sink’s Neighbor Problem” hinders the applications of Wireless Sensor Networks (WSNs). Owing to the heavier traffic burden they bear, the nodes close to the sink tend to dissipate their energy faster than others. They die earlier when their energy is used up. The energy efficiency is vital for WSNs because it is unpractical for the sensor nodes to be recharged. An Energy Efficient sink Deployment Scheme (EEDS) aiming at extending the lifespan for wireless sensor networks is proposed. It alleviates the energy consumption imbalance by adopting a mobile sink and regulating its route. Specifically speaking, a mobile sink moves in the monitor area with a certain rate and lingers at the fixed stops. During its move, the sensor nodes collect the data and store them in their memorizers. It is proved that the network lifetime is extended to be approximately six times compared with the static sink used only schemes via mathematical analysis. Besides, the scheme is extensible when the monitor area is enlarged. Finally, the energy efficiency of EEDS is evaluated through comparisons with the strategies only using static or mobile sinks. Experiment results show that EEDS has a good energy efficiency and can extend the network lifespan to be approximately 6 times compared with that of static-sink-used-only schemes. Besides, it is prolonged to be 148 percent of that of strategy using the mobile sink such as GMRE.

In Wireless sensor Network, the sensor nodes transmit critical information over the network; therefore, security services, such as, authentication and pair wise key establishment between sensor nodes and mobile sinks, are important. However, the problem of authentication and pair wise key establishment in sensor networks with MSs is still not solved in the face of mobile sink replication attacks. For the basic probabilistic and q-composite key pre distribution schemes, an attacker can easily obtain a large number of keys by capturing a small fraction of the network sensor nodes, making it possible for the attacker to take control of the entire network by deploying a replicated mobile sink, preloaded with some compromised keys to authenticate and then initiate data communication with any sensor node. To solve this problem, we have developed a general framework that permits the use of any pair wise key pre distribution scheme as its basic component, to provide authentication and pair wise key establishment between sensor nodes and MSs. The new framework requires two separate key pools, one for the mobile sink to access the network, and one for pair wise key establishment between the sensors.

Wireless sensor networks (WSNs) include small sensor nodes with battery and processing power and limited memory units then improving power consumption is a major design challenge for any sensor network. In this paper, a new algorithm for routing in the wireless sensor network is proposed using the ultra-innovative algorithm for bacterial Foraging and mobile sink, which leads to energy efficiency. In the proposed method, the number of sensor nodes is determined according to two criteria: the amount of energy on the battery surface and the distance to the sink ahead, which leads to the formation of regular clusters in the network. Nodes adopt a multi-step routing scheme within the network to communicate with the sink. also, the mobile sink is used to balance the load and help consume uniform energy throughout the network. The simulation results show better performance of the proposed method in terms of energy consumption by 17.99%, throughput rate by 30.04%, end-to-end delay by 46.04%, signal-to-noise ratio by 32.81%, delivery rate successfully Data to the sink is 0.80 times higher than the AFSRP (Artificial Fish Swarm Routing Protocol).

In wireless sensor networks (WSNs), sensor nodes near static sink will have more traffic load to forward and the network lifetime will get largely reduced. This problem is referred to as the hotspot problem. Recently, adopting sink mobility has been considered as a good strategy to overcome the hotspot problem. Despite its many advantages, due to the dynamic network topology caused by sink mobility, data transmission to the mobile sink is a challenging task. To achieve efficient data dissemination, nodes need to reconstruct their routes toward the latest location of the mobile sink, which weakens the energy conservation aim. In this paper, we proposed an energy-efficient cluster-based dynamic routes adjustment approach (EECDRA) which aims to minimize the routes reconstruction cost of the sensor nodes while maintaining nearly optimal routes to the latest location of the mobile sinks. The network is divided into several equal clusters and cluster heads are selected within each cluster. We also set some communication rules that manage routes reconstruction process accordingly requiring only a limited number of nodes to readjust their data delivery routes toward the mobile sinks. Simulation results show that the mobile sinks for reducing reconstruction of route have improved the energy efficiency and prolonged lifetime of wireless sensor network.

In this paper, we propose and discuss two types of algorithms to improve energy efficiency in Wireless Sensor Networks. An efficient approach for extending the life of a network is known as “sensor clustering” in wireless sensor networks. In proposed algorithms, the study area where sensor nodes are randomly distributed is divided into clusters. In each cluster, the sensor that is the closest to the cluster center and has the highest residual energy is chosen as the cluster head. To make this choice, a greedy approach and artificial neural network methods are applied. In addition, to reduce the energy consumption of cluster heads, a mobile sink is used. The list of routes to be used by the mobile sink is calculated with the genetic algorithm. According to the route information, the mobile sink moves to the clusters and initiates the data collection process for each cluster. We compared our models according to the round value at which all sensor nodes run out of energy and the energy consumption by the network per round. Simulation results show that the proposed models increase the energy efficiency and extend the network lifespan.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Maximization of lifetime of wireless sensor network (WSN) is an emerging area of research in present scenario. Many authors are performing their research work, so that they could achieve lower energy consumption for the sensor network which leads to increase in lifetime of network. Our work focuses on sink mobility in a fixed trajectory within a wireless sensor network while sensed data are required to be collected. We divide the whole sensor network into two different regions as direct communication area (DCA) and multi-hop communication area (MCA). Sensors node that lies in DCA is at one hop count distance from the sink node trajectory while sensors within MCA are at more than one hop count from the trajectory. We considered all the sensors that are within DCA as subsink nodes. During sink mobility, whenever a subsink is closer to the mobile sink node, then it starts transmitting its data to the sink node. But those sensors that are within MCA needs to search an appropriate sink node for sending its sensed data to the subsink so that on further stage, that subsink could provide those data to the sink node whenever it is in nearest proximity of that subsink. Basically, a sink node is the ultimate destination for the data while a subsink node acts as a relay node for the nodes that are within MCA. So our work is to find out the appropriate subsink node from a given set of subsinks. For doing so, we used location-aided routing on global positioning system (GPS)-enabled sensors and sink node. Our work is validated through simulation experiments using NS-2.

Wireless sensor networks (WSN) consist of large number of sensor nodes. These small and low cost sensor nodes are deployed in monitoring region of interest. Since sensors perform multiple functions such as data gathering and data transmission results in energy depletion, these effects the network lifetime. The nodes having tendency to fail due to energy depletion is the critical issue in WSN. Due to wide number of applications of WSN, it is required to enhance the life time of entire network. Proposed method in this paper is aimed to optimize the energy dissipation by modifying the CH (Cluster Head) selection approach in LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm and avoids the energy-hole problem considering mobility to the sink node which helps to prolong the lifetime of entire network.