Wireless sensor networks (WSNs) distribute hundreds to thousands of inexpensive micro-sensor nodes in their regions, and these nodes are important parts of Internet of Things (IoT). In WSN-assisted IoT, the nodes are resource constrained in many ways, such as storage resources, computing resources, energy resources, and so on. Robust routing protocols are required to maintain a long network lifetime and achieve higher energy utilization. In this paper, we propose a new energy-efficient centroid-based routing protocol (EECRP) for WSN-assisted IoT to improve the performance of the network. The proposed EECRP includes three key parts: a new distributed cluster formation technique that enables the self-organization of local nodes, a new series of algorithms for adapting clusters and rotating the cluster head based on the centroid position to evenly distribute the energy load among all sensor nodes, and a new mechanism to reduce the energy consumption for long-distance communications. In particular, the residual energy of nodes is considered in EECRP for calculating the centroid′s position. Our simulation results indicate that EECRP performs better than LEACH, LEACH-C, and GEEC. In addition, EECRP is suitable for networks that require a long lifetime and whose base station (BS) is located in the network.

An Efficient Centroid-Based Routing Protocol for Energy Management in WSN-Assisted IoT

Wireless sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to form a network (well known as wireless sensor networks) to complete the IoT Infrastructure. These deployed wireless sensors are with limited energy and processing capabilities. The IoT infrastructure becomes a key factor to building cyber-physical-social networking infrastructure, where all these sensing devices transmit data toward the cloud data center. Data routing toward cloud data center using such low power sensor is still a challenging task. In order to prolong the lifetime of the IoT sensing infrastructure and building scalable cyber infrastructure, there is the requirement of sensing optimization and energy efficient data routing. Toward addressing these issues of IoT sensing, this paper proposes a novel rendezvous data routing protocol for low-power sensors. The proposed method divides the sensing area into a number of clusters to lessen the energy consumption with data accumulation and aggregation. As a result, there will be less amount of data stream to the network. Another major reason to select cluster-based data routing is to reduce the control overhead. Finally, the simulation of the proposed method is done in the Castalia simulator to observe the performance. It has been concluded that the proposed method is energy efficient and it prolongs the networks lifetime for scalable IoT infrastructure.

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Building Scalable Cyber-Physical-Social Networking Infrastructure Using IoT and Low Power Sensors

For the past few decades, Wireless sensor networks (WSNs) disseminate less expensive sensor nodes (SN’s) in its area, where nodes plays significant role in Internet of Things (IoT). WSN based IoT, nodes are generally resource constraints in numerous ways, like energy, storage, computing resources and so on. Effectual routing protocols (RP) are essential for longer lifetime and to attain superior energy utilization. Here, a novel energy efficient k- centroid Lion optimization based routing protocol (kEE-LOP) to enhance network performance. Anticipated kEE-LOP comprises three essential factors: distributed behaviour of lion based cluster formation technique to facilitate nodes organization, novel algorithm for constructing adaptive clusters based on lion migration and k-nodes centroid based cluster head rotation to distribute load to all sensor nodes eventually, thereby a novel mechanism for reducing energy utilization for distance communications. In specific, residual energy of nodes is measured with kEE-LOP for long distance communication. Specifically, nodes’ residual energy is measured as kEE-LOP for computing CH centroid’s position. Simulation results specifies kEE-LOP outperforms superior than LEACH, LEACH-C. As well as, kEE-LOP is more appropriate for networks that is essential for longer lifetime.

An Efficient Lion Optimization Based Cluster Formation and Energy Management in WSN Based IoT

The landslide phenomenon’s has become a serious problem in Himalayan regions to killed many people and also thrashed their whole living areas. All these tragedies triggered by environment changes and gathered by sensor nodes. Due to some reasons, sensor nodes are not possible to transfer sensed data to base station. The link failure is a major problem which creates data losses in the wireless sensor networks. In this paper, we propose an efficient lossless landslide monitoring (LLM) system. The proposed system consists of two phase, such as data gathering and handling phase. In data gathering phase, we use modified gray wolf optimization algorithm for clustering technique, which provides link aware routing. In data handling phase, we use an iterative dichotomize-3 (ID-3) based decision making algorithm for landslide prediction. The proposed system tested with the five different environmental sensors, such as rain gauge, incline meter, crack meter, tilt meter, and piezo meter for gathering environmental information’s. The simulation result shows that the delivery ratio of proposed LLM system is 30% higher, drop ratio is 27.5% lower, energy consumption is 11.25% lower, routing overhead is 13% lower and throughput is 19% higher than existing systems.

Link Aware Routing Protocol for Landslide Monitoring Using Efficient Data Gathering and Handling System

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be true in many actual WSNs—the wireless links among nodes are often subject to all kinds of disturbance, obstruction, etc., and may transmit with different success rates. This paper proposes a new routing strategy, named Adaptive Network Coding Routing (ANCR). ANCR firstly establishes a routing path with the traditional network coding routing (NCR), and then applies the neighborhood search algorithm to adaptively determine nodes’ coding opportunities based on the links’ transmission success rates, with the target of reducing the total number of transmission. The simulation results show that, in WSNs with different-success-rate links, ANCR can reduce the network delay by about 50%, and increase the network throughput by about 67%, compared with the traditional NCR.

ANCR-an Adaptive Network Coding Routing Scheme For WSNs with Different-Success-Rate Links

Wireless sensor networks (WSN), as a new type of environment monitoring system, has became a hot research topic in recent years. This paper mainly focuses on the problem of signal conflicting in WSN. The link quality (quality between two neighboring nodes) can be guaranteed by the layout of network, but, the end-to-end routing quality can not be guaranteed in the same way because of random signal conflicting (even if CSMA/CA is used in WSN). The end-to-end routing will have higher performance if the routing has lower signal conflicting probability. So, the main work of this paper is designing a routing protocol to find out the routing with the lowest signal conflicting probability. This paper proposed a Minimum conflicting probability routing protocol (MCR) in link quality guaranteed WSN. Firstly, MCR combines the degree value with workload of nodes, forming a new degree and cache based routing metric (DBM). Secondly, MCR finds out the best routing by the Random Walk theory on basis of DBM. The simulation results show that, MCR protocol is more effective to avoid the signal conflicting, it has a higher end-to-end reliability and a more stable network throughput than other routing protocols in the same link quality guaranteed WSN.

Research on routing protocol facing to signal conflicting in link quality guaranteed WSN

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Research on routing protocol facing to signal conflicting in link quality guaranteed WSN