Higher Data Delivery Rate Research Articles

IRDFPR-CMDNN: An energy efficient and reliable routing protocol for improved data transmission in MANET

Mobile Ad hoc Networks (MANET) are structure less, autonomous wireless networks with mobile nodes that dynamically establish data transmission connections. Due to dynamic topological change, MANET routes are unbalanced and break repeatedly. Hence, providing efficient and reliable data delivery with effective utilization of network resources is a challenging issue to be considered in MANET. This paper proposes an instant-runoff Ranked Decision Forests Probit Regression-based Connectionist Multilayer Deep Neural Network (IRDFPR-CMDNN) for efficient data transmission and higher data delivery with a minimum end-to-end delay. This IRDFPR-CMDNN method performs route identification, data delivery, and route maintenance with more than three layers. Then the mobile nodes are sent to the input layer of the Connectionist Multilayer Deep Neural Network. In hidden layer 1, the Instant-runoff Ranked Decision Forests algorithm is applied for classifying the mobile nodes depending on the residual energy and load capacity. With selected mobile nodes, the Probit Regression is applied for finding the nearest neighboring nodes in the second hidden layer based on the link quality and received signal strength for route path establishment. Then multiple paths for routing are established from source to destination node and start to perform the data transmission. If link failure occurs during the data transmission, another alternative route with better link quality is selected for routing. In this way, energy-efficient data transmission is performed from source to destination with a higher data delivery rate and minimal time consumption. Experimental evaluation is carried out on energy consumption, packet delivery ratio, packet drop rate, throughput, and end-to-end delay with varying numbers of mobile nodes and data packets. Simulation results show that the IRDFPR-CMDNN technique effectively enhances data delivery, throughput and minimizes energy consumption, packet loss rate, delay with respect to conventional methods.

Secure and Energy Efficient Trust Aware Routing Protocol in IoT using the Optimized Artificial Neural Network: SEETA-IoT

Nowadays, Internet of Things (IoT) has been widely used in many areas such as surveillance of battlefield, controlling of industry, pipeline monitoring, ecological monitoring, healthcare and medical science, defense and military affairs, etc. To make better IoT based applications, secure routing plays an imperative responsibility for Device-to-Device (D2D) data packet transmission. The most essential inventiveness behind this research is to design a Secure and Energy Efficient Trust Aware (SEETA) routing protocol in terms of detecting the fail/malicious nodes by utilizing the Artificial Neural Network (ANN) using Particle Swarm Optimization (PSO) Algorithm. To discover a secure and energy-efficient route, the trust-based approach is integrated with GOA and ANN. Therefore, this research focuses on the security enhancement of the IoT network using the concept of trust-based route maintenance mechanism. In IoT networks, communicating sensor nodes have limited energy resources and a rapid energy reduction of communicating nodes leads to the creation of energy holes in the network. To overcome this problem, an energy-efficient mechanism of trust aware approach is also one of the essential objectives of IoT routing protocols. To avoid unnecessary network failure and extra energy consumption rate, we had designed a model using the concept of PSO based ANN with a novel fitness function and for involvement in the routing process, sensor nodes should fulfill the fitness criteria; otherwise, the system considers as affected communicating nodes. From the experiments conducted, it is proved that the proposed SEETA routing protocol using the PSO and ANN, achieves significant performance improvement over the existing schemes in terms of security, energy efficiency and packet transmission rate. Besides, SEETA routing protocol also reduces the number of intermediates nodes in D2D route discovery mechanism for IoT networks which helps to decrease the energy consumption rate and also offers a proficient approach to discover secure routing support with maximum routing capacity and end to end data transmission rate. The QoS performance parameter of our routing protocol is analyzed with standard and existing work with several routing protocols and the experimental results validating the concept of optimized ANN. The experimental results indicate that the introduced SEETA routing protocol provides 8.74% less energy consumption rate and high data delivery rate and data delivery rate is improved by 92% as compare to exiting works.

ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks.

Wireless Sensors Networks (WSNs) are an essential element of the Internet of Things (IoT), and are the main producers of big data. Collecting a huge amount of data produced by a resource-constrained network is a very difficult task, presenting several challenges. Big data gathering involves not only periodic data sensing, but also the forwarding of queries and commands to the network. Conventional network protocols present unfeasible strategies for large-scale networks and may not be directly applicable to IoT environments. Information-Centric Networking is a revolutionary paradigm that can overcome such big data gathering challenges. In this work, we propose a soft-state information-centric protocol, ICENET (Information Centric protocol for sEnsor NETworks), for big data gathering in large-scale WSNs. ICENET can efficiently propagate user queries in a wireless network by using a soft-state recovery mechanism for lossy links. The scalability of our solution is evaluated in different network scenarios. Results show that the proposed protocol presents approximately 84% less overhead and a higher data delivery rate than the CoAP (Constrained Application Protocol), which is a popular protocol for IoT environments.

Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review.

During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

RECRP: An Underwater Reliable Energy-Efficient Cross-Layer Routing Protocol.

A reliable energy-efficient routing protocol plays a key role in underwater data transmission. In the face of acoustic communication challenges in underwater wireless sensor networks (UWSNs), including long propagation delay, topology change, limited energy, and communication voids, we propose RECRP, a Reliable Energy-efficient Cross-layer Routing Protocol to achieve high data delivery rate. RECRP is a location-free single-copy protocol. The information of the physical layer such as Doppler scale shift measurement, Received Signal Strength Indication (RSSI), etc. are adopted to estimate the distance, thus no extra hardware is needed for localization. Moreover, the overhead introduced by redundant packets is avoided with the single-copy mechanism. To improve the two-hop packet delivery rate and balance energy consumption among adjacent nodes, an optimal max–min method is proposed that dynamically controls transmission power and channel frequency. Furthermore, a surface to bottom routing establishment method is also adopted to handle communication voids. Compared with depth-based routing (DBR) and hop-by-hop vector-based forwarding (HH-VBF), RECRP is more energy-efficient with a higher delivery rate.

Load balanced and efficient data collection protocol for wireless sensor networks

As the state-of-the-art data collection protocol for wireless sensor networks (WSNs), collection tree protocol (CTP) has been applied to many practical applications. But, it can lead to load imbalance and data congestion as to each node chooses the most optimal path in CTP. To address the problem of load imbalance, this paper presents and evaluates V-CTP, a novel data collection protocol for wireless sensor network based on the CTP. V-CTP considers the number of child nodes, and use a virtual metric (v-eetx) to choose a path. We implement V-CTP and evaluate its performance on an indoor test-bed with 30 to 100 Telosb nodes. The experiment results show that V-CTP protocol can effectively solve the problem of load imbalance and maintain high data delivery rate as well as CTP. In addition, V-CTP achieved desirable energy-efficiency, prolonged the life of networks and improved the performance of networks.

Load balanced and efficient data collection protocol for wireless sensor networks

As the state-of-the-art data collection protocol for wireless sensor networks (WSNs), collection tree protocol (CTP) has been applied to many practical applications. But, it can lead to load imbalance and data congestion as to each node chooses the most optimal path in CTP. To address the problem of load imbalance, this paper presents and evaluates V-CTP, a novel data collection protocol for wireless sensor network based on the CTP. V-CTP considers the number of child nodes, and use a virtual metric (v-eetx) to choose a path. We implement V-CTP and evaluate its performance on an indoor test-bed with 30 to 100 Telosb nodes. The experiment results show that V-CTP protocol can effectively solve the problem of load imbalance and maintain high data delivery rate as well as CTP. In addition, V-CTP achieved desirable energy-efficiency, prolonged the life of networks and improved the performance of networks.

DRS: Dynamic Region Scheduling for Solar-powered Wireless Sensor Networks

Energy restriction is crucial for large-scale application of Wireless Sensor Networks (WSNs). To achieve sustainable operations, the wireless sensor nodes need to harvest energy from the surrounding environment. This paper proposed the Dynamic Region Scheduling (DRS) for solar-powered WSNs. In DRS, all the wireless sensor nodes are allocated into different regions with different transmitting priorities and the collected data could only be delivered from lower priority region to higher or the same priority regions. Each sensor node would renew its region ID dynamically by changing its Mask-identifier (MI) and thus would help the data delivered directly to the sink without any redundancies. Compared with another two routing algorithm SEHRP and EAHC, the results shows that DRS has achieved a significant performance improvement of a higher data delivery rate, a lower energy consumptions as well as lower data loss ratio and data redundancy ratio, which would be greatly helpful to prolong the lifetime of WSN.