A FRAMEWORK FOR CONSERVING POWER IN MANETS

The idea of using controllable relay nodes for designing mobile systems has been explored by several researchers. The main objective of this paper is to design a framework for deploying relay nodes with controlled mobility to conserve power in MANET. To meet out this proposal, a heterogeneous network consisting of traditional nodes with limited energy and relay nodes with more energy resources are considered. The deployment of relay nodes are based on the following two methods: 1. Min-Total, aims to minimize the total energy consumption of all the traditional nodes in the network. 2. Min-Max, aims to minimize the energy consumed by a traditional node in the network. The solution of these two methods can be used to prioritize each individual node in the network. The trade-offs involved in deploying an increasing/decreasing fraction of relay nodes, varying node weights, varying epoch duration is analyzed. This framework aims at dynamically combining both Min-Total and Min-Max based on the characteristics and requirements of the network

Towards video streaming in IoT environments: vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues

ASURVEY ON CLUSTER BASED LOAD BALANCINGAPPROACHESFOR WIRELESSSENSOR NETWORK

Wireless sensor network (WSN) is becoming a very interesting field of research in recent days. It has wide area of research due to various issues caused by the hardware capabilities of sensing nodes such as memory, power, and computing capabilities. One of the major issues is to concentrate on the energy consumption of the sensing node which determines the lifetime of the network. One of such problem is called Hot-spot problem, in which the best channel to the sink are overloaded with traffic and thus causing the nodes to deplete their energy quicker than the energy of other nodes in the network. Clustering algorithms along with sink mobility widely support for equal distribution of the load in the network. In order to overcome this problem various load balancing algorithms are discussed for improving the lifetime of the network

Controlled Mobility in Mobile Sensor Networks: Advantages, Issues and Challenges

International audienceRecently, wireless self-organizing networks are attracting a lot of interest in the research community. Moreover, in the last decade many mobile devices have appeared in the market. Exploiting mobility in a wireless environment, instead of considering it as a kind of disturbance, is a fundamental concept that the research community is beginning to appreciate now. Of course, the advantages obtainable through the use of the mobility imply the knowledge of the different types of mobility and the way to include it in the management architecture of the wireless networks. In this work we claim that mobility and wireless sensor networks can be considered as two synergetic elements of the same reality. For this purpose, we sketch a macro-classification of the different objectives which can be pursued by controlled mobility. Moreover, we identify and highlight the interactions between this specific type of mobility and the layers of the control stack. Lastly, this paper reports a case study in which we show how controlled mobility can be exploited practically

Performance Evaluation of Target Trajectory and Angular Position Discovery Methods in Wireless Sensor Networks

In sensor networks Target Tracking defines how accurate a targets position can be measured. We consider both stationary target and mobile target. Since the mobile target is unknown, the mobile sensor controller utilizes the measurement collected by a wireless sensor network in terms of the mobile target signal2019;s time of arrival (TOA).We proposed time of arrival2 (TOA2) algorithms which consider time to live (TTL). We investigate the correlations and sensitivity from a set of system parameters. We derive the minimum number of mobile sensors that are required to maintain the resolution for target tracking in a mobile sensor network (MSN).The simulation results demonstrate the tracking performance can be improved by an order of magnitude with the same number of sensors when compared with that of the static sensor environment

An Effective Mobile Sensor Control Method for Sparse Sensor Networks

In this paper, we propose an effective mobile sensor control method, named DATFM (Data Acquisition and Transmission with Fixed and Mobile node) for sparse sensor networks. DATFM uses two types of sensor nodes, fixed node and mobile node. The data acquired by nodes are accumulated on a fixed node before being transferred to the sink node. In addition, DATFM transfers the accumulated data efficiently by constructing a communication route of multiple mobile nodes between fixed nodes. We also conduct simulation experiments to evaluate the performance of DATFM

Spreadable Connected Autonomic Networks (SCAN)

A Spreadable Connected Autonomic Network (SCAN) is a mobile network that automatically maintains its own connectivity as nodes move. We envision SCANs to enable a diverse set of applications such as self-spreading mesh networks and robotic search and rescue systems. This paper describes our experiences developing a prototype robotic SCAN built from commercial, off-the-shelf hardware, to support such applications. A major contribution of our work is the development of a protocol, called SCAN1, which maintains network connectivity by enabling individual nodes to determine when they must constrain their mobility in order to avoid disconnecting the network. SCAN1 achieves its goal through an entirely distributed process in which individual nodes utilize only local (2-hop) knowledge of the network's topology to periodically make a simple decision: move, or freeze in place. Along with experimental results from our hardware testbed, we model SCAN1's performance, providing both supporting analysis and simulation for the efficacy of SCAN1 as a solution to enable SCANs. While our evaluation of SCAN1 in this paper is limited to systems whose capabilities match those of our testbed, SCAN1 can be utilized in conjunction with a wide-range of potential applications and environments, as either a primary or backup connectivity maintenance mechanism

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios