This work presents a high-resolution, ray-tracing-based channel modeling for Low Earth Orbit (LEO) satellite-to-ground links in a suburban environment at X-band. Using simulations conducted in Wireless InSite, we develop a parametric channel model that characterizes both large- and small-scale fading effects across different satellite elevation angles. Large-scale fading incorporates attenuation due to terrain-induced shadowing and dynamic environmental factors such as weather conditions, and is compared with 3GPP NTN channel model. Additionally, we quantify link degradation resulting from ground station (GS) antenna misalignment, considering both fixed single-element and electronically steerable phased-array antennas. Small-scale fading is modeled by fitting a shadowed and non-shadowed Rician distribution to the fading statistics at various satellite elevations. To the best of our knowledge, this is the first study to propose a comprehensive elevation-aware channel model for satellite-to-ground propagation at X-band, integrating ray-traced environmental dynamics, elevation-dependent fading, and phased-array beam misalignment effects.

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario.

Channel modeling in radio communication is crucial, especially for emerging technologies like LoRa. While LoRa shows promise for Unmanned Aerial Vehicle (UAV) telemetry and C2 channels, its progress in sub-GHz bands is hindered by duty cycle limitations. LoRa operating at 2.4 GHz has no such restrictions and can achieve higher data rates. However, the propagation characteristics and goodput performance of 2.4 GHz LoRa have not been well studied. In this work, we present path loss, round-trip time, and goodput measurements for air-to-ground communications (A2G) with 2.4 GHz LoRa. We compare our results with well-known path loss models, where we observe a relative fit by the Walfisch-Bertoni and Two-Ray models in rural-like areas. Our results demonstrate that for spreading factors SF=7 and SF=10, a link latency of less than 50 ms could be achieved, but it fails to meet the minimum required data rate required for a Command and Control (C2) link.

Abstract Privacy-preserving smart meter data collection and analysis are critical for optimizing smart grid environments without compromising privacy. Using homomorphic encryption techniques, smart meters can encrypt collected data to ensure confidentiality, and other untrusted nodes can further compute over the encrypted data without having to recover the underlying plaintext. As an illustrative example, this approach can be useful to compute the monthly electricity consumption without violating consumer privacy by collecting fine-granular data through small increments of time. Toward that end, we propose an architecture for privacy-preserving smart meter data collection, aggregation and analysis based on lattice-based homomorphic encryption. Furthermore, we compare the proposed method with the Paillier and Boneh–Goh–Nissim (BGN) cryptosystems, which are popular alternatives for homomorphic encryption in smart grids. We consider different services with different requirements in terms of multiplicative depth, e.g. billing, variance and nonlinear support vector machine classification. Accordingly, we measure and show the practical overhead of using the proposed homomorphic encryption method in terms of communication traffic (ciphertext size) and latency. Our results show that lattice-based homomorphic encryption is more efficient than Paillier and BGN for both multiplication and addition operations while offering more flexibility in terms of the computation that can be evaluated homomorphically.

We study the performance exhibited by the transport protocols, Transport Control Protocol (TCP) and QUIC, over realistic satellite networks. We propose a novel methodology, which combines real implementation (exploiting virtualization techniques) and simulation, to carry out systematic and repetitive experiments. We modify the default operation of the ns-3 framework and we integrate the dynamism that characterizes satellite communication links, particularly Low Earth Orbit (LEO). We carry out a thorough assessment over different setups, changing the operating frequency band and packet buffer lengths. In addition, we ascertain the impact of using the multi-streaming feature that QUIC integrates. The results show that QUIC yields lower delays than TCP, although it might suffer from higher jitter in particular setups. In addition, the results evince that using multiple streams in QUIC does not yield a relevant gain for the default Round-Robin (RR) scheduler. We propose more appropriate scheduling strategies, which are able to yield better performances with unbalanced traffic. Even if the behavior of transport protocols over non-terrestrial-networks might not be always appropriate, the obtained results evince that QUIC can definitively bring benefits when compared to TCP. Furthermore, we have shown that optimal scheduling policies yields a fairer performance when using multiple flows, having unbalanced traffic loads.

In this work we address the challenging problem of manipulating a flexible link, like a rope, with an aerial robot. Inspired by spraying tasks in construction and maintenance scenarios, we consider the case in which an autonomous end-effector (e.g., a spray nozzle moved by a robot or a human operator) is connected to a fixed point by a rope (e.g., a hose). To avoid collisions between the rope and the environment while the end-effector moves, we propose the use of an aerial robot as a flying companion to properly manipulate the rope away from collisions. The aerial robot is attached to the rope between the end-effector and the fixed point. Assuming no direct control of the end-effector (e.g., when operated by a human), we design a reactive and fast motion planner for the aerial robot. Grounding on the theory of Forced Geometric Fabrics, we design a motion planner that generates trajectories to drive the aerial robot to follow the end-effector, while manipulating the rope to avoid collisions in cluttered environments. To include the complex behavior of the flexible link, we propose a rope model that estimates its real-time state under forces and position-based interactions, as well as collisions with obstacle surfaces. Finally, we evaluate the system behavior and the motion planner performance in simulations, as well as in real-world experiments on an original spray painting application.

Low Earth Orbit (LEO) satellite constellations have been identified for new massive access networks, as a complement to traditional cellular ones, due to their native ubiquity. Despite being a feasible alternative, such networks still raise questions on their performance, in particular regarding the delay and queuing management under realistic channels. In this work, we study the queuing delay of a single satellite-to-ground link, considering a Land Mobile Satellite (LMS) channel in LEO with finite buffer lengths. We analyze the trade-off between delay and packet loss probability, using a novel model based on Markov chains, which we assess and extend with an extensive analysis carried out by means of system level simulation. The developed tools capture with accuracy the queuing delay statistical behavior in the S and Ka frequency bands, where LEO communications are planned to be deployed. Our results show that we can use short buffers to ensure less than 5-10% packet loss, with tolerable delays in such bands.

The introduction of Ultra-Reliable Low-Latency Communications (URLLC) in 5th Generation (5G) networks is expected to introduce a new set of commercial services with stringent communications requirements such as 10 millisecond delay and 99.99% reliability. Cellular Vehicle-to-Everything (C-V2X) based High Density Platooning (HDPL) technology is considered as a URLLC use case for improving traffic efficiency and fuel saving by packing vehicles close together at high speeds. However, conventional cellular resource scheduling and mobility management mechanisms are not able to meet the requirements of HDPL. In this work, we propose sidelink group scheduling and mobility for platoons and present a simulation model to improve the management of the group resource allocation. Our results show that a careful selection of the group resource switching time significantly increases the overall network Quality of Service (QoS) provided to platoons. However, further 5G URLLC enhancements will be needed in addition to group scheduling to meet the requirements of HDPL for safe and efficient operation.

Peer-to-peer distributed storage systems can be instrumental to develop solutions able to store the massive amounts of data generated by the Internet of Things (IoT) users. Given the higher probability of node failures, losses in the communication channels, and limited resources of devices compared to centralized storage solutions, it is key to minimize data retrieval time, while also maintaining high resiliency in the system. We propose a method based on random linear network coding (RLNC) for data storage and retrieval and the use of Kademlia for our peer-to-peer design to address these challenges. We analyze the performance of the proposed RLNC-based method theoretically as well as the traditional Kademlia in terms of data retrieval time and resiliency to node failures and channel losses. We use PeerSim to simulate the proposed method. Our theoretical analysis and simulation results show that the proposed RLNC-based method significantly outperforms traditional Kademlia for our core performance metrics. These gains in resiliency and data retrieval time are achieved while also reducing the data storage time for a wide region of operation. Our simulations show that only if the redundancy of the RLNC-based scheme is significantly increased (> 100 % redundant RLNC packets), then a small degradation (<; 10 %) in data storage time occurs.

The amount of open satellite data has increased tremendously in recent years, simultaneously with a continuing decrease in the price of high performance cloud computing. This can be combined with machine learning methods to perform crop type classification in the agricultural sector. In this paper, we propose a data processing chain for processing multitemporal Sentinel-1 SAR data, and show how the temporal patterns of agricultural fields can be visualized to provide a valuable overview prior to classification. We then investigate the performance of 6 machine learning methods for crop type classification of 12 crop types based on 44333 fields, and achieve an overall accuracy of (94.02 ± 0.25)% with an RBF SVM classifier. The dataset used is a subset of all the fields of the chosen crop types in Denmark in 2019, which comprises a total of 289810, or 49.34% of all fields in the country for the 2019 season. The entire data processing chain is based on open data and free open source software, thereby minimizing the cost of practical applications and future work for both industry and academia. All code used for the paper is available on GitHub.

The increasing demand for applications of the Internet of Things (IoT) has produced the emergence of use cases with challenging scenarios for cellular access. In such use cases, Low Earth Orbit (LEO) small satellite constellations provide global coverage for regions isolated from terrestrial infrastructure, but pose difficulties for routing given their dynamic nature. In this paper, we study ad hoc distance vector routing for LEO small satellite constellations. We provide a qualitative evaluation based on performance metrics such as scalability, robustness and energy consumption. Then, we propose Ad hoc On-Demand Distance Vector (AODV) as an applicable protocol and compare it with flooding with an implementation of a network simulator in C# for various constellation sizes and routing options. Our results show that routing across the constellation seam and the active route timeout play a critical role in the Packet Delivery Ratio (PDR) and packet throughput. Although reasonably functional, we conclude that AODV should be complemented with proactive routing in tightly controlled polar constellations, since pre-computed routing tables will perform better under specific conditions.

Given the growing demand of high-performance communication solutions on high-constraint Low Earth Orbit (LEO) small satellites, in this work we propose a set of designs of patch antenna arrays for CubeSats in the X band that are suitable for satellite-to-ground and Inter-Satellite Link (ISL) communications for LEO. In our analysis we consider the unit cell geometry as well as the array design as parameters. The parameter space is evaluated using the Finite Element Method (FEM) analysis software CST Microwave Studio. We transfer the designs evaluated in CST to AGI Systems ToolKit (STK) to evaluate the influence of each parameter on the link budget for different passes and attitude noise conditions. Our results show that it is possible to achieve a 12-15dB gain in the link budget for the given scenarios. We also observe that such antenna arrays can provide satisfactory attitude inaccuracy compensation with a phase shifter quantization as low as 2 bits.

The system design of autonomous Unmanned Aerial Vehicles (UAVs) poses several challenges for the wireless communication concerning performance, resiliency, and scalability. The UAV system is typically restricted by flying time limitations due to energy constraints and can benefit from coordination of tasks among UAVs. It may further profit from cooperation between UAVs to share information context, maintain flying formation, extend communication range etc. In this paper, we review communication schemes for clusters of autonomous UAVs cooperation. Performance metrics for assessing those schemes are investigated. We provide a survey of candidate wireless communication technologies considering a power line inspection target application. Performance measurements and UAV related channel modeling of those candidate technologies are reviewed. We present how the requirements of the UAVs communication system are identified through the application specification.

Given the increasing demand of small satellite-based services for the Internet of Things (IoT), we study the potential benefits of employing Random Linear Network Coding (RLNC) as a transport layer scheme in nanosatellite constellations for IoT. We present a timing analysis of the end-to-end transmission process for the Reliable Datagram Protocol (RDP) and RLNC at the transport layer to compare their performances. It includes the effect of these protocols in terms of delay for different IoT scenarios such as event detection/localization, asset tracking and sensor data aggregation. We verify with numerical simulations, that for our schemes ideal bit rates that minimize the total delay are possible. We observe 50-65% delay reductions for RLNC under the same operational conditions as RDaP.

This paper introduces a scalable framework to coordinate the net load scheduling, sharing, and matching in a neighborhood of residential prosumers connected to the grid. As the prosumers are equipped with smart appliances, photovoltaic panels, and battery energy storage systems, they take advantage of their consumption, generation, and storage flexibilities to exchange energy with neighboring prosumers through negotiating on the amount of energy and its price with an aggregator. The proposed framework comprises two separate multi-objective mixed integer nonlinear programming optimization models for prosumers and the aggregator. Prosumers' objective is to maximize the comfort level and minimize the electricity cost at each instant of time, while aggregator intends to maximize its profit and minimize the grid burden by matching prosumers' supply and demand. The evolutionary nondominated sorting genetic algorithm-III (NSGA-III) is employed to generate a set of feasible nondominated solutions to the optimization problem of each individual prosumer and the aggregator. As a bilateral negotiation between each prosumer and the aggregator results in significant computational and communication overhead, a virtual power plant is introduced as an intermediator on behalf of all prosumers to proceed the negotiation with the aggregator in a privacy-preserving noncooperative environment, where no private information is shared. Hence, an automated negotiation approach is embedded in the framework, which enables the negotiators to reactively negotiate on concurrent power and price using private utility functions and preferences. To converge to an acceptable agreement, the negotiation approach follows an alternating-offer production protocol and a reactive utility value concession strategy. The effectiveness of the framework is evaluated by several economic and environmental assessment metrics through a variety of numerical simulations.

Blockchain has proved itself to be tamper resistant and secure. It is increasingly getting attention from companies changing from centralized to decentralized systems. This paper proposes a system for identity and access management using blockchain technology to support authentication and authorization of entities in a digital system. A prototype demonstrates the application of blockchain in identity and access management using the Hyperledger Fabric framework. It provides a proof of concept based on a use case concerning Electronic Health Records from the healthcare domain where an immutable and auditable history is desired for data concerning patients. Basic authentication and authorization operations are able to execute in 2-3 seconds with an initial size of blockchain of about 3.8 MB covering physicians in Denmark.

Connected vehicles (CVs) are one of the critical components of intelligent transportation systems. CVs enable any vehicle to act as a smart node that collects and shares information on vehicles, roads, and their surroundings. This information can then be distributed to other vehicles via vehicle-to-vehicle (V2V) communication, and also to road users via vehicle-to-human (V2H) communication, for an improved driving experience. The information can also be forwarded toward traffic control systems via vehicle-to-infrastructure (V2I) communication, for improved traffic management and road safety. Making use of of connected vehicles in intelligent transportation systems will revolutionize the way we drive. Many issues, however, need to be resolved to achieve better performance of connected vehicles. Improvements relate to data processing and storage, the development of standards and regulations across all platforms, design and deployment of new communication protocols and system architectures, and the creation and introduction of new services and applications.

Geospatial data analytics is emerging as a promising technology in decision support systems in many application domains. This paper presents results from a study on the use of elastic cloud computing to provide geospatial data analytics in an efficient way. The analytics is applied to bulks of satellite data from multi-spectral optical sensors. The system design includes data integration, data caching, and a job manager to orchestrate the data analytics workflow. The system is validated in a case study focusing on precision agriculture, where a simple harvest detection algorithm is implemented to identify the time of harvest for crop fields. The performance of the underlying cloud computing infrastructure is evaluated and the benefit of scalingthe cloud horizontally when bulk satellite data becomes available is discussed.

This paper investigates the possibility of elevating SMS (Short Message Service) to support a cloud-based vehicle-Tracking platform. A secure application protocol over SMS is introduced to circumvent the security issues that may succumb the SMS. We propose a cost-effective Internet of Things (IoT) solution to countries dominated with GSM mobile infrastructure taking into consideration technology usage and the mobile network infrastructure available. A commercial off-The-shelf (COTS) IoT device such as the Raspberry Pi single board computer is depicted relying on GPS-GSM technologies envisioned as the in-vehicle device. Furthermore, a cloud web-platform is built involving the de-facto modern web-Applications standards and carefully tailored concerning the security aspects. The cost-effectiveness of our solution results from the use of COTS components, open source software, and cheap SMS subscription packages.

Smart grid applications belong to a diverse set of technologies, which combine behaviors and actions of all grid actors. Building such application is a challenging task requiring modeling, integrating, and validating different grid aspects efficiently. The design flow should adapt to the grid's requirements ranging from basic appliance functions to complex load scheduling algorithms and their integration's consequences. This paper tackles such challenges by proposing a framework to describe smart grid elements formally along with defining their interactions. The framework provides a smooth way for upgrading grid components independently and evaluate their performance. A case study utilizing an infrastructure of electric vehicles is demonstrated to validate the framework and prove its applicability in modeling smart grid applications. The case study enables a demand response service that provides a solution to the coordinated charging scheduling problem of electric vehicles.

Software applications are built to run on different devices with dissimilar platforms. The traditional software development process allows to build applications that run on a specific platform in which the development cycle becomes time-consuming and costly due to rebuilding the application for individual platform. To solve this problem, we propose a model-driven methodology for the development of a data visualization application targeting multiple platforms such as desktop computers, tablets and smart phones. The methodology flows from a Platform-Independent Model (PIM) to a Platform-Specific Model (PSM) and ends by executable models for multiple platform. The methodology paves the way to build an automatic synthesis tool that relieves the software developers from the repetitive work. The methodology is validated through a case study that visualizes data from smart grid domain. The case study is built in collaboration with the European research project SmartHG.

Event-driven demand response programs reward consumers for shifting their electricity loads upon requests from an aggregator, a balance responsible party, or a distribution system operator. In order to have any significant impact on the smart grid, the flexibility exhibited on the demand-side needs to be aggregated from a large number of consumers. This poses significant scalability challenges for the ICT infrastructure that controls flexible electricity loads. This paper reports from the European research project SEMIAH. The project aims to design a scalable infrastructure for residential demand response. The study presents progress towards the system design of a centralized load scheduling algorithm for controlling home appliances over time. The demand response system takes the power grid constraints and satisfaction of consumers into account. Simulation results from a case study quantify the potential impact of a residential demand response program.

The realization of the smart grid depends critically on the successful engagement of consumers in adapting their energy consumption behavior to the intermittent generation of renewable energy. The engaged consumers participate in demand response programs in which they provide a shift of electricity consumption based on incentives provided. However, understanding the concept of load shifting and the decision when to shift consumption is quite complex for most consumers and it may interfere with daily practices leading to inconveniences. Therefore, there is a strong need for efficient decision-support systems to help consumers to shift or not to shift electricity consumption. This paper presents a conceptual framework aimed at system designers and developers of electricity load shifting applications. The framework is validated by implementation of a prototype that provides planning and decision support for consumers. The prototype provides insight into possible load shifting capability by providing feedback on the CO2 emission intensity of energy production, thus encouraging consumers to change behavior and consume energy in a more sustainable way.

Demand response is proposed as a solution to handle the fluctuations in the power supply in a scenario with higher penetration of renewable energy sources. While demand response already offers a positive business case in certain domains it still lacks matureness in other areas, especially in the residential domain. This paper aims at providing a feasibility study of residential demand response by designing and validating a novel multi-modal Building Energy Management System (BEMS) that enables demand response provision and energy efficient control from residential buildings. The proposed consumer-centric BEMS monitors the building performance and its surroundings, interacts with the residents, optimally controls local Distributed Energy Resources (DERs) and provides demand response to an aggregator. The design decisions and the consequent architecture are detailed. A prototype of the envisioned BEMS has been developed and deployed in a testbed - a 12-storey residential building located in Denmark. The prototype performance, the data monitoring capabilities, interaction with the residents and controllability of local DER of the BEMS are demonstrated.

Compression of electrocardiograms (ECG) in wireless environments, with diagnostic quality, has shown limited potential. This lack of quality preservation, using Wavelet Transform (WT), is due to the fact that the multiple levels of detail that can be achieved in the time domain are not exploited. In the present work, we propose to fully exploit the wavelet capability to operate at different levels of signal detail at different time scales. WT with an appropriate Compressed Sensing (CS) matrix is used in the electrode nodes of body sensor networks to encode and compress the ECG. Then, the signal is reconstructed using a basis pursuit denoise algorithm. Preservation of the diagnostic quality by means of standardized metrics is then tested for multiple wavelet bases and levels. High quality ECGs from 50 healthy patients are used to statistically show that diagnostic quality preservation is possible even at high compression rates. In these cases suitable ECG wavelets are required.

It was long time ago, when the Internet was a basic network used for data and information sharing. The evolutionary process of the Internet started with connecting people and then with the increasing popularity of it and with the emerging concept of smart systems, device-to-device communications within smart systems dawned. And now; by taking the advantage of another emerging concept: mobility, we are able to connect any object to that network and build the Internet of Things. In this article, we propose, implement, and discuss the outcomes of such a system, which uses RFID tags to identify objects, a reader to detect the tags, a computer platform to connect them to the next generation IPv6 Internet and a DNS server to provide mobility and accessibility.

The SmartHG project goal is to develop a suite of integrated software services (the SmartHG Platform) aiming at steering residential users energy demand in order to: keep operating conditions of the electrical grid within given healthy bounds, minimize energy costs, and minimize CO2 emissions. This is achieved by exploiting knowledge (demand awareness) of electrical energy prosumption of residential users as gained from SmartHG sensing and communication infrastructure. This paper describes such an infrastructure along with user demand patterns emerging from the data gathered from ~600 sensors installed in ~40 homes participating in SmartHG test-beds.

Smart grids offer an indisputable business opportunity for system operators and energy traders to engage in demand response programs. Hereby these actors may profit from trading flexibility provided at the prosumer side on the energy markets. This paper discusses the system design challenges for an information and communication technology system infrastructure facilitating DR for residential prosumers. It presents the SEMIAH framework for providing a scalable infrastructure for residential DR built on a component-based architecture with the virtual power plant at the hearth of the system. The paper examines the possible impact of deploying an automated residential DR program on the quality and stability of a low voltage grid.

In the prevailing smart grid, the Home Area Network (HAN) will become a critical infrastructure component at the consumer premises. The HAN provides the electricity infrastructure with a bi-directional communication infrastructure that allows monitoring and control of electrical appliances. HANs are typically equipped with wireless sensors and actuators, built from resource-constrained hardware devices, that communicate by using open standard protocols. This raises concerns on the security of these networked systems. Because of this, securing a HAN to a proper degree becomes an increasingly important task. In this paper, a security model, where an adversary may exploit the system both during HAN setup as well as during operations of the network, is considered. We propose a scheme for secure bootstrapping of wireless HAN devices based on Identity-Based Cryptography (IBC). The scheme minimizes the number of exchanged messages needed to establish a session key between HAN devices. The feasibility of the approach is demonstrated from a series of prototype experiments.

The Energy Internet is the vision of performing intelligent automation in the smart grid using Internet-based technologies. This vision complies with embracing the residential domain into the smart grid, since it facilitates the reuse of the existing Internet connection in the home. Stakeholders such as Energy Service Companys (ESCOs) and Distribution System Operators (DSOs) can then acquire meter data from residential homes, where the residential consumer can gain potential cost savings by using their services. However, residential consumers have concerns about their privacy when metering devices monitor home appliance usage that potentially can reveal their habits without their consent. This paper discusses architectural design challenges and presents a consumer-centric system architecture with incentives for the residential consumers, ESCOs and the DSOs to participate. The architecture is based on a Service-Oriented Architecture (SOA) using web services that follow the Representational State Transfer (REST) architectural style. The ESCOs provide intelligent automation through home-oriented and grid-oriented web services that optimise for the residential home and DSO, respectively. The consumer can control the privacy enforcement through a Home Energy Management System (HEMS) that negotiates the information content of the meter data with a management entity in the cloud. OAuth 2.0 is adapted for the consumer to authorise web services to access meter data from the management entity.

To enhance network scalability and increase network lifetime in large-scale wireless sensor networks (WSNs), clustering has been recognized as an effective solution for hierarchical routing, topology control and data aggregation. Inspired by the collective behavior of flocks and schools, we propose a Bio-inspired self-organizing Low-Complexity Clustering (B-LCC) algorithm for large-scale dense WSNs. The B-LCC algorithm does not require sensor locations, time synchronization nor any priori knowledge of the network. It is completely distributed and can achieve a well-distributed cluster heads. The processing time complexity of the B-LCC algorithm is O(1) per cluster, which outperforms most of the existing clustering algorithms as they have processing time complexity of O(n) per node in the worst case. Additionally, the B-LCC algorithm has a stable performance in topology control and the formed topology is robust to node failure.

To support reliable real-time applications in Body Sensor Networks (BSNs), it is necessary to develop an efficient and robust routing protocol. However, it is challenging due to the specific radio propagation characteristics, dynamic network topology, variable link quality caused by body movements or environments, extremely low transmission power and limited battery among many others. In this paper, we propose Reactive Virtual Coordinate Routing protocol (RVCR) to tackle the challenges in BSNs by making a good use of the specific radio propagation and link characteristics on the human body. The basic idea of RVCR is that it efficiently exploits the temporary high-quality link opportunities resulting from the body movements and the environments to effectively forward packets towards the destination. RVCR achieves a stable packet delivery ratio (PDR) of more than 99% independent of the body movements and the environments at the lowest possible transmission power. Besides high PDR, RVCR has a stable and small average number of hops per packet, which makes it energy efficient. Additionally, RVCR can keep the same performances even in case of severe link failures.