Deploying a networked set of robots is an effective way to serve applications in environments where human intervention is impossible or possess risks. For example, a team of robots can assist ...rescuers to map, navigate indoor hazardous areas in rescue operation. Collaboration among the robots is very essential in these applications in order to efficiently achieve the aimed goals in a timely manner. Realising such a collaborative operation autonomously in the absence of GPS services is a challenge. This study tackles this challenge assuming sensors/landmarks are present in the deployment area. Each sensor/landmark requires a specific number of robots to perform certain tasks. A spatial–temporal coverage solution is pursued to maintain connectivity and overcome the shortage of available robots. Dynamic coverage problem is formulated as potential fields where landmarks and nodes exert virtual forces among each other based on coverage demand and overlapped area. The proposed approach has been validated through extensive simulation using NS3 simulator and real experimentation using EV3 robots. The proposed approached has shown maximal task satisfaction compared with random waypoint and very close behaviour compared with a centralised approach (Hungarian method).
In recent years, the field of programming Unmanned Arial Vehicles (UAVs) has gained significant attention from researchers due to their substantial potential in various applications, including ...surveillance, inspection, and critical situations like examining buildings that are burning or collapsing. For this purpose, drones that have several sensors installed would be of good use in constructing a 3D map and localizing most of the objects within a certain area or room, which is also referred to as Simultaneous Localization and Mapping (SLAM). However, installing these sensors would harden the mission of the drone since it would mean more power consumption, more computations, and less navigation flexibility. For this reason, monocular visual SLAM has become the trend, which refers to using a sole camera to build the map and locate the objects in each scene. This approach introduces new challenges, one of the most crucial challenges is estimating the depth (i.e., distances within an image) of each scene from a 2D image. For this task, Deep Learning (DL) models have been considered as a solution for this problem, and with the continuous development in DL and the computational resources that can carry out the expensive training of DL models, it was shown that a 3D map reconstruction is possible utilizing 2D images. This work investigates the performance of a combination of different SLAM and depth estimation models implemented on a commercial drone. The main goal is to carry out a comparison between different methodologies of depth estimation that support monocular 3D SLAM algorithms.
Advancement in energy harvesting technologies has inspired new ways of supporting industrial applications especially in harsh environment with almost everlasting free power. There are several sources ...such as solar, wind, vibration, radio frequency (RF), etc. and each source has its own characteristics. RF poses unique characteristics which make it a good candidate for specific applications such as indoor sensor networks. However, due to the scarcity of harvested power, it is essential to manage this power efficiently taking into consideration the quality of service requirements. In this paper, we classified the power management techniques into two major classes: soft and hard techniques; soft techniques such as MAC protocols, while hard techniques RF harvester circuit design. In this work, we survey the literature for the existing soft power management techniques for Radio Frequency (RF) energy harvesting based sensor networks. Then, we shed light on the major challenges and open issues in building a sustainable RF energy harvesting based wireless sensor networks.
As moving to the EVs is essential due to the environmental cues, we need to solve the problems and undertake the challenges of the EVs to expedite this move. One of these challenges is the long ...recharging time compared to the traditional refueling process and the consequence of this issue is overcrowding in charging stations. To solve this problem, a charging management system is needed. However, an effective charging management system requires efficient communication networks that facilitate the exchange of information between the EVs, charging stations and the management server. In this work, we study the feasibility of exchanging EV's data using V2V and V2I communications infrastructure considering Al Khobar city in Saudi Arabia as a case study. SUMO GUI and OMNET++ simulators have been used to set up the environment. The simulation results show that the minimum data throughput requirements are 0.83,1.11, and 59.19 Bytes for V2V, V2I, and I2I respectively. This paper has contributed to finding the minimum requirement of the data throughputs of the communication links in vehicular communication.
Smart grids are becoming ubiquitous in recent time. With the progress of automation in this arena, it needs to be diagnosed for better performance and less failures. There are several options for ...doing that but we have seen from the past research that using Wireless Sensor Network (WSN) as the diagnosis framework would be the most promising option due to its diverse benefits. Several challenges such as effect of noise, lower speed, selective node replacement, complexity of logistics, and limited battery lifetime arise while using WSN as the framework. Limited battery lifetime has become one of the most significant issues to focus on to get rid of it. This article provides a model for replenishing the battery charge of the sensor nodes of wireless sensor network. We will use the model for sensor battery recharging in an efficient way so that no nodes become out of service after a while. We will be using mobile charger for this purpose. So, there may be some scope for improving the recharge interval for the mobile charger as well. This will be satisfied using optimum path calculation for each time the charger travels to the nodes. Our main objectives are to maximize the nodes battery utilization, distribute power effectively from the energy harvester, and minimize the distance between power source and cluster head. The simulation results show that the proposed approach successfully maximizes the utilization of the nodes battery while minimizes the waiting time for the sensor nodes to get recharged from the energy harvester.
This paper investigates an optimal cross-layer joint routing and scheduling problem for WSN with periodic data collection. The problem is formulated as an Integer Linear Program (ILP) model such that ...a joint scheduling and routing is developed to maximize network lifetime. An ILP model for Energy-Efficient Distributed Schedule-Based (EEDS) protocol is proposed. The main objective of the ILP model is to build an energy efficient joint routing tree and TDMA scheduling framework considering the EEDS assumptions. The ILP model is solved for different network configurations. The results obtained by the ILP model are compared with the EEDS protocol simulation results.
Unmanned Aerial Vehicles (UAVs) use is increasing daily, which results in increasing their security threat landscape. A major security threat to availability for network-based devices is Denial of ...Service (DoS) and Flooding attacks, which impact the system resources and disturb the network communications. For Mobile Ad hoc Networks (MANETs), there are multiple previous works on DoS attacks implementation and mitigation using different techniques. However, the work is limited for 2D networks with little focus on detecting and mitigating such attacks on 3D networks of Flying AdHoc Networks (FANETs). This study focuses on the RREQ flooding attack on a 3D network of drones and proposes a threshold-based detection and mitigation mechanism that considers the drones limited resources. The simulation experiments demonstrate a 34% throughput improvement and 3% energy saving.
Wireless Sensor Network (WSN) enables pervasive, ubiquitous, and seamless communication with the physical world. This paper investigates an optimal cross-layer joint routing and scheduling problem ...for WSN with periodic data collection. The problem is formulated as an Integer Linear Program (ILP) model such that a joint scheduling and routing is developed to maximize network lifetime and minimize delay. In this paper, an ILP model with multi objective cost function is proposed. The proposed ILP model represents the operation of Energy-Efficient Distributed Schedule-Based (EEDS) protocol. The ILP model is solved for different network configurations. The optimal solutions assuming different objectives are compared.
Growing Unmanned Aerial Vehicle (UAV) market trends and interest in potential uses such as monitoring, visual inspection, object detection, and path planning have shown promising results using ...machine learning techniques. However, UAV adoption faces several challenges in real-life scenarios as lowaccuracy sensors are involved in the identification, tracking, and localization of UAVs. In order to overcome the aforementioned challenges, this paper proposes an intelligent machine learningbased system coupled with computer vision (CV) to detect objects and localize UAVs equipped with just a monocular camera. The experimental results using the Telo DJI drone demonstrate that the proposed methodology can detect, track objects, and localize the drone with high accuracy. The system's ability for automated monitoring in real environments can lend its uses for urban traffic, logistics, and security applications.
Wireless sensor networks can provide effective means for monitoring and controlling a wide range of applications. Recently, tremendous effort was directed towards devising sensors powered from ...ambient sources such as heat, wind, and vibration. Wireless energy transfer is another source that has attractive features that make it a promising candidate for supplying power to wireless sensor nodes. This paper is concerned with characterizing and modeling the charging time and received signal strength indicator for wireless energy transfer system. These parameters play a vital role in deciding the geometry of sensor network and the routing protocols to be deployed. The development of communication protocols for wireless-powered wireless sensor networks is also improved with the knowledge of such models. These two quantities were computed from data acquired at various coordinates of the harvester relative to a fixed position of RF energy source. Data was acquired for indoor and outdoor scenarios using the commercially available PowerCast energy harvester and evaluation board. Mathematical models for both indoor and outdoor environments were developed and analyzed. A few guidelines on how to use these models were suggested. Finally, the possibility of harvesting the energy from the ambient RF power to energize wireless sensor nodes was also investigated.
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