In distributed multi-robot exploration, effective communication among mobile robots significantly impacts the efficiency of area coverage, mapping accuracy, and collaborative decision-making. Typically, robots share their information map based on dynamically established network topologies to balance communication overhead and information accuracy. This paper comparatively analyzes three dynamic topology construction strategies for mobile robots exploring unknown environments: the Relative Neighborhood Graph (RNG), the k-Relative Neighborhood Graph (K-RNG), and the K-Nearest Neighbors (KNN). RNG is a graph-based approach that maintains adaptive links based on geometric proximity, efficiently managing connectivity while reducing redundant data exchanges. The K-RNG generalizes RNG by allowing a tunable number of points within the geometric neighborhood, providing flexible control over network density. In contrast, KNN selects neighbors by prioritizing robots in immediate proximity, quickly adapting to local density changes. We conduct extensive simulations to evaluate these strategies against critical Quality-of-Service (QoS) metrics, including packet delivery ratio and latency. Results demonstrate RNG's effectiveness in reducing unnecessary data transmissions while maintaining stable connections, K-RNG's performance tunability based on the parameter$k$, and the strong dependency of KNN's performance on the choice of$k$.

Robust vehicle authentication is essential in order to ensure an effective audit of vehicle-to-vehicle (V2V) communications. However, most existing approaches rely on a centralized infrastructure to access both authorities' certificates andrevocation lists, thus making them ineffective in dynamic and infrastructureless environments. In this paper, we highlight this critical limitation, and propose a method which enables the vehicles to update their local authentication databases independently from infrastructure availability. Our approach aim to allow vehicles to perform V2V authentication using locally stored data, in order to ensure continuity of secure communications even when disconnected from the Infrastructure. We further analyze the probability of successful authentication under two scenarios, which are the first with up-to-date databases, and the second with outdated ones. The analytical results show that the authentication probability decreases to below$\mathbf{7 5 \%}$after$\mathbf{3 0}$hours of disconnection with long-lived certificates, while updates keep it above 90% in highway scenarios, even with short-lived certificates. These findings demonstrate the feasibility of maintaining reliable V2V authentication outside the infrastructure coverage, and point out the necessary improvements for evolving towards secure and auditable V2V communications.

This paper introduces a distributed multi-robot exploration system that combines bio-inspired flocking dynamics with machine learning-powered connectivity prediction to enable efficient unknown environment mapping while maintaining robust communication networks. Designed for emergency response scenarios where operational timelines and network reliability are critical, the approach enriches a previous work to implement a role-adaptive framework in which robots dynamically alternate between exploration-focused and connectivity reinforcement behaviors based on real-time signal strength assessments. Explorers employ frontier detection to explore the unknown areas enhanced with RSSI-aware frontier choosing mechanism, while supporters autonomously position themselves using Flocking inspired behavior, forming communication relays. A comparative evaluation of machine learning models trained on FIT-IoT-Lab-collected signal propagation data enables the system to predict RSSI values in different environments. The system's innovation lies in its integration of flocking-based swarm coordination with data-driven connectivity prediction, addressing the challenge of maintaining network stability while exploring unknown areas efficiently. In addition, the system is designed to operate within the strict computational constraints typical of embedded robotic platforms, leveraging lightweight machine learning models that enable real-time connectivity predictions.

The rise of connected vehicles has transformed transportation by enhancing mobility, safety, and driving comfort. However, ensuring secure and trustworthy communications in vehicular networks remains a challenge due to the risks of malicious activities, privacy breaches, and unauthorized access. This paper aims to address these challenges by evaluating and comparing existing authentication schemes used in vehicular communications. Specifically, the article focuses on analyzing their efficiency, security, and applicability for audit systems in Vehicle-to-Everything (V2X) communications. The main objectives of this study are to provide a clear taxonomy of authentication strategies, evaluate their ability to preserve anonymity and integrity while ensuring accountability, and identify protocols suitable for robust audit mechanisms. Through qualitative and quantitative analysis, this paper highlights the strengths and limitations of current solutions, emphasizing aspects like scalability, privacy preservation, and infrastructure dependency. Findings indicate that combining Public Key Infrastructure (PKI)-based methods with Blockchain technology can yield secure and transparent communication solutions. Nevertheless, significant hurdles remain in scenarios lacking infrastructure support. The key contribution of this work consists in identifying authentication protocols that successfully balance security, efficiency, and privacy–while still enabling effective audits–thereby laying the groundwork for designing reliable, trust-oriented audit systems in tomorrow’s vehicular networks, including outside the infrastructure coverage.

Internet of Things (IoT) applications generate tremendous amounts of data streams which are characterized by varying Quality of Service (QoS) indicators. These indicators need to be accurately estimated in order to appropriately schedule the computational and communication resources of the access and Edge networks. Nonetheless, such types of IoT data may be produced at irregular time instances, while suffering from varying network conditions and from the mobility patterns of the edge devices. At the same time, the multipurpose nature of IoT networks may facilitate the co-existence of diverse applications, which however may need to be analyzed separately for confidentiality reasons. Hence, in this paper, we aim to forecast time series data of key QoS metrics, such as throughput, delay, packet delivery and loss ratio, under different network configuration settings. Additionally, to secure data ownership while performing the QoS forecasting, we propose the FeDerated Temporal Sparse Transformer (FeD-TST) framework, which allows local clients to train their local models with their own QoS dataset for each network configuration; subsequently, an associated global model can be updated through the aggregation of the local models. In particular, three IoT applications are deployed in a real testbed under eight different network configurations with varying parameters including the mobility of the gateways, the transmission power and the channel frequency. The results obtained indicate that our proposed approach is more accurate than the identified state-of-the-art solutions.

The cloud-to-edge-to-IoT continuum represents a seamless flow of data processing and management, spanning from centralized cloud services to distributed edge computing and interconnected IoT devices. This paradigm can become very challenging in real implementations, especially in the presence of multiple stakeholders using proprietary and heterogeneous software and hardware. The Horizon Europe NEPHELE project proposes the virtualization of IoT devices through a specific software stack called Virtual Object Stack (VOStack) that promotes openness and interoperability. In this paper, we present an implementation of VOStack using W3C Web of Things (WoT) standard in a post-disaster domain for two different types of IoT devices: a ground robot (Turtlebot2) for navigation and mapping in an unknown environment, and a Raspberry Pi 3 acting as a wireless sensor network gateway. We propose an application graph for the resulting hyper-distributed application (HDA) and present our first implementation to validate the proposed solution.

The ubiquitous nature of mobile devices equipped with radio communication technologies made the collection of data a commonplace especially for studying human mobility. The collection of such datasets forms the intermediate results in many scientific research projects. Therefore, with the lack of datasets, collecting and publishing data should be seriously addressed since several scientific research is based on the gathering and analysis of measurement data. In this paper, we introduce the PILOT dataset, a Privacy-preserving data collectIon of wireLess cOmmunication Technologies. The dataset is a collection of four jointly collected information in different mobility contexts. It includes three wireless communication technologies: WiFi probe-responses, BLE (Bluetooth Low Energy) beacons, and LoRa (Long Range Radio) packets, plus additional information: Acceleration, Roll, and Pitch, all collected at the same time. We provide the keys to reproduce such data collection and share the datasets already collected. The dataset is collected for approximately 90 hours, with a size of 200 MB using FiPy devices from Pycom and it is uploaded to GitHub. The dataset’s utility is validated through the application of a classification machine learning model that determines the real-life situation of devices through the communication links monitored in different scenarios with an accuracy of $94 \%$. Thus, we believe that such dataset is important for human mobility studies and applications of integrated sensing systems since it offers a new form of a classified collected data that does not exist in the already published datasets.

Deploying communication networks in rural sce-narios is challenging due to the sparse population density, geographical obstacles, lack of infrastructure, and daily life situations. Inspired by the PRoPHET protocol, we present the R-PRoPHET protocol to provide an intermittently connected network in a rural context, where the variable mobility of vehicles requires flexibility to model the encounters between nodes. Our protocol incorporates information about the time between encounters in the past to make smooth adjustments in parameters that impact the decision to relay messages. The R-PRoPHET protocol implements two forwarding strategies to manage the trade-off between the delivery ratio and the number of messages relayed along the network. Results showed that R-PRoPHET increases the delivery ratio by up to 20% and reduces the average latency by up to 16% compared to PRoPHET. The overhead produced by R-PRoPHET is around two times more than PRoPHET, which is part of the trade-off of increasing the average delivery ratio and reducing the average latency.

Recent droughts have once again proven the threat of climate change on our food production systems. Building efficient and frugal soil monitoring networks may be part of the solution. Providing real-time data on temperature, humidity and soil moisture can help decision makers to optimize irrigation, fertilization and pest control schedules. Additionally, such networks can be used to raise alert in case of a disease outbreak allowing for a swift response. One of the great challenges to make these tools widely available lays in building frugal and efficient data collection. In part, these networks should have low energy requirements to allow for deployment in remote areas and reduce maintenance costs. Networks with large amounts of transmissions often involve redundancies in the transmitted data, which results in high energy consumption for low utility. For example, the measurement of temperature at a point A and another measurement of temperature at a second point close to A. Using a well-chosen prediction algorithm, estimations of a node's measurements could be an acceptable alternative to transmitting the real values. This work extends our previous work designed for leveraging correlation between two nodes to explore this correlation between multiple nodes over a multi-hop network. Results show that we can reach up to 250 times less data while not losing the information which validates our approach as we can see in the evaluation section.

IoT devices, constrained by limited resources, must balance energy consumption and performance, with the communication module often being the largest energy consumer. Among various communication technologies, Bluetooth Low Energy (BLE) is notable for its efficient design and performance. However, despite recent BLE optimizations, the Neighbor Discovery Process (NDP) remains energy-intensive. We propose a new approach that uses a modern microcontroller's multicore architecture to dynamically adjust the scanning duration based on the number of devices to be discovered. Our simulations show that avoiding a fixed scanning duration by concluding the NDP once the required devices are found can significantly reduce energy consumption. Validation through simulations and real-world experiments demonstrates up to 50% energy savings compared to a fixed 1-second scan using a single core during the NDP.

The protocols for vehicular networks are mostly developed, tested and evaluated through simulations in network simulators first before implementing and experimenting them in the real-world. For simulating vehicular networks, various mobility datasets are made publicly available by SUMO that can be imported into network simulators (e.g., OMNeT++, NS-3). The development of protocols for vehicular networks requires analyzing some common metrics (such as distribution of vehicles, contact duration between vehicles and time to meet new neighbors). Therefore, researchers are high likely to implement and analyse these metrics as a first step. In this paper, we analyse the common required metrics for vehicular networks using network simulator OMNeT++ with Veins framework and SUMO vehicular mobility datasets of Ireland urban and highway scenarios for rush and non-rush hours. We have made our code available online at GitHub that will be time-saving for researchers who start developing protocols for vehicular networks. We believe such analysis results can help in designing communication or dissemination protocols in vehicular networks and understanding vehicular traffic for further applications.

Broadcasting is an important routing strategy for message delivery in a VANET. However, the broadcast storm caused by loads of duplicate data packets can be severe, which leads to intense contention among vehicle nodes and a large number of dropped packets. In this case, the efficiency of message delivery is limited. This paper considers a broadcast routing protocol specially designed for urban areas with dense traffic and a huge amount of data traffic, and proposes a Preferred-Zone based broadcast (PreZcast) protocol. To alleviate the broadcast storm and improve the efficiency of message delivery, PreZcast only allows neighbors located within a Preferred Zone (PreZ) of a source node to rebroadcast a received packet. The PreZ is chosen based on the initial location and the distribution of two-hop neighbors of the source node. To further validate PreZcast in a more realistic and irregular topology, we evaluate PreZcast with real datasets issued from Bologna road traffic. Moreover, MultiPoint Relay (MPR) is introduced to further reduce duplicate packets in the network. Simulation results show that as compared with the Flooding protocol, PreZcast approximately improves 15% of broadcast efficiency and reduces 65% of packets transmitted with 5% more energy consumption, while PreZcast with MPR improves 10% of broadcast efficiency and reduces 70% of packets transmitted with only 0.5% more energy consumption.

The study of human mobility becomes more and more crucial these days in transportation studies, urban planning, crowd mobility behaviors, and even more. In this paper, we propose a novel approach for studying human mobility by building a light machine learning (ML) model using observation of wireless networking information from WiFi and Bluetooth low energy (BLE) that are today naturally present in everyday devices such as mobile phones. Our goal is to build a mobility classification system using communicating devices of any kind with low processing complexity. However, we propose a new approach for mobility classification using a real dataset of WiFi and BLE beacons collected over one year for around 90 hours in different scenarios and conditions. The first model (B-model) aims to identify the status of a device if stationary or mobile. Then a complementary model (M-model) is applied to determine a more precise real-life situation of the device, which could be a Home, Office, Bus, Train, etc. The results show that decision-tree-based ensemble ML algorithms like LGBMClassifier and XGBClassifier gave the best results, in terms of accuracy and f1 score for both models with an accuracy of 99% and 94% respectively, confirming the capability of classifying mobility context from only WiFi and BLE data. We believe that such an approach could be leveraged for studying human mobility and an important step towards the large deployment of mobility-based applications by leveraging everyday mobile phones.

Wireless sensor networks are a precious tool for numerous use cases: studying the fauna and flora, measuring various metrics and so much more. Here we focus specifically on wireless multihop networks, in which the funneling effect is too often responsible of the unbalance of energy consumption, causing in turn nodes failures. In an attempt to mitigate the effects of funneling, the scientific literature proposes among others traffic reduction based on data estimation. The lead idea being that if two phenomena are correlated, then maybe we could estimate the way one goes based on the way the second one do. This effectively reduces the amount of data nodes may have to transmit while preserving the information. We propose in this article a similar method, but here not only limited to a pair of nodes. With our scheme we can reduce the traffic of several nodes over a whole multihop route of communications in a network. We have implemented that scheme and experimented with real hardware which shows good results.

Contact Tracing (CT) is an old, recognized epi-demiological tool, and since a digital variant is now within reach, a variety of smartphone-based solutions have been rapidly developed and deployed since 2020, with mixed results and amid controversies. Yet, achieving reliable and effective digital CT at large scale is still an open problem. In this work, we contribute with an open source software platform on top of which various CT solutions can be quickly developed and tested. More specifically, we design PEPPER, which jointly leverages Bluetooth Low Energy (BLE) and Ultra Wide Band (UWB) radios for contact detection, combined with the DESIRE privacy-preserving CT protocol. We show that PEPPER+DESIRE can operate on cheap physical tokens based on low-power microcontrollers, opening new use-cases with less personal, potentially disposable devices, that could be more widely used. We also evaluate the complementarity of Bluetooth and UWB in this context, via experiments mimicking various scenarios relevant for CT. Compared to BLE-only CT, we show that UWB can decrease false negatives (e.g., in presence of human body occlusion), meaning that more actual contacts will be found, a key benefit from an epidemiological viewpoint. Our results suggest that, while PEPPER+DESIRE improves precision over state-of-the-art, further research is required to harness UWB-BLE synergy for CT in practice. To this end, our open source platform (which can run on an open-access testbed) provides a useful playground for the research community.

Nowadays, to improve animal well-being in livestock farming applications, a wireless video sensor network (WVSN) can be deployed for surveillance and livestock monitoring to early detect injury or Asiatic hornets attacks [1]. They are composed of small embedded video and camera motes that capture video frames periodically and send them to a specific node called a sink. Sending all the captured images to the sink consumes a lot of energy on every sensor and may cause a bottleneck at the sink level. Energy consumption and bandwidth limitation are two important challenges in WVSNs because of the limited energy of nodes and the medium scarcity. In this work, we exploit the Spatio-temporal correlation between neighboring nodes to reduce the number of captured frames. For that purpose, Synchronization with Frame Rate Adaptation SFRA algorithm is introduced where overlapping nodes capture frames in a synchronized fashion every$N-1$period, where$N$is the number of overlapping sensor nodes. The results show more than 90 % data reduction, surpassing other techniques in the literature at the level of the number of sensed frames by 20% at least.

With the increase of Internet of Things (IoT) applications, securing their communications is an important task. In multi-hop wireless networks, nodes must unconditionally trust their neighbours when performing routing activities. However, this is often their downfall as malicious nodes can infiltrate the network and cause disruptions during routing. We grant nodes the ability to evaluate the behaviour of their neighbours and, through consensus inspired from blockchain's miners, agree on the credibility of each node. The resulting metric is expressed as a node's reputation allowing, in the case of a malicious node, to isolate it from network operations. By illustrating this in an AODV-like multi-hop routing protocol, we can influence route selection no longer based solely upon the shortest number of hops, but also the highest overall reputation. Simulation results revealed that our approach can decrease packet drop rates by ≈ 48% in a static context when subjected to multiple black hole attacks compared to the original routing protocol.

Internet of Things (IoT) devices generate a tremendous amount of time series data that is extremely dynamic, heterogeneous and time dependent. Such types of data introduce significant challenges for the real-time prediction of QoS metrics of IoT applications with different traffic characteristics. To this end, in this paper, we propose a temporal transformer model and a unified system to predict several QoS metrics of heterogeneous IoT applications when they communicate with the Edge of the network. The transformer model also leverages an attention module to provide a solution for both short-term and long-term sequence prediction of QoS metrics that allows to better extract any time dependencies. In particular, in our framework, we firstly generate a set of datasets containing real-time traffic information of five different IoT applications such as Heating, Ventilation, and Air Conditioning (HVAC), lighting, Voice over Internet Protocol (VoIP), surveillance and emergency response using the 802.15.4 access technology and the RPL routing protocol. Following, we perform the data cleaning, downsampling and pre-processing of the datasets and we construct the QoS datasets, which include four QoS metrics, namely throughput, packet delivery ratio, packet loss ratio and latency. Finally, we evaluate the transformer model through extensive experimentation using both short-term and long-term dependencies and we show that our model can guarantee a robust performance and accurate QoS prediction.

Wireless Sensor Networks (WSN) are limited by the characteristics of the Radio Access Technologies (RAT) their are based on. We call a wireless multi-hop network composed of nodes able to use several RAT a Multiple Technologies Network (MTN). Nodes must manage the RAT and route selection, in a local and distributed way, with an suitable communication protocol stack. Nodes may share multiple common RAT with multiple neighbors. Thus the devices' heterogeneity of technologies has to be taken into account by each of the stack's layer. In this article, we introduce our custom Routing Over Different Existing Network Technologies protocol (RODENT), designed for MTN. It is capable of dynamically (re)selecting the best RAT and route based on data requirements evolving over time. RODENT is based on a multi-criteria route selection via a custom lightweight TOPSIS method from our previous work [1]. For an evaluation of performance, we implemented a functional prototype of RODENT on Pycom FiPy devices. Results show that RODENT enables multiple data requirements support and energy savings, while increasing effective coverage.

In vehicular networks, vehicles carry various types of data that need to be offloaded to the RoadSide Units (RSUs) through Vehicle-to-Infrastructure (V2I) communications when vehicles come into their coverage. Since, RSUs are not widely deployed, vehicles have intermittent connectivity with RSUs. The data that vehicles carry to offload could be urgent data (such as accident data of nearby incident or emergency health data) that needs to be offloaded to the RSUs as soon as possible. Therefore, the consideration of Quality of Service (QoS) provisioning is imperative for data offloading in vehicular networks. In this paper, we propose V2I-Q, a V2I data offloading scheme with QoS provisioning by using three QoS functions: traffic classification, overload control and admission control. Traffic classification organizes the data into three priorities: high, medium and low. Overload control avoids overloading the RSUs to enable it to receive high priority data as soon as possible. Admission control allows RSU s to stop servicing existing vehicles offloading low priority data in order to receive high priority data from other vehicles. The performance evaluation shows that V2I-Q is able to offload more high priority data by incurring lower maximum offloading delay as compared to the traditional V2I data offloading schemes.

In vehicular networks, RoadSide Units (RSUs) are not available everywhere, therefore, it is not possible for vehicles to stay connected with RSUs all the time and to send their data directly to RSUs anytime using Vehicle-to-Infrastructure (V2I) data offloading. Hence, in these cases, Vehicle-to-Vehicle (V2V) communications are used to offload data to RSUs through other vehicles. Data to offload could be urgent (e.g., accident data), therefore, it is important to consider the Quality of Service (QoS) provisioning. In this paper, we propose a QoS-aware data offloading scheme for vehicular networks with QoS provisioning (DOVEQ) that considers both V2I and V2V data offloading. DOVEQ models the connectivity of vehicles with RSUs and vehicles, offloading capacity and estimation of reaching RSUs. It provides QoS using traffic classification, overload control and admission control. The performance evaluation of DOVEQ shows that DOVEQ outperforms other schemes by offloading more amount of important data with lesser offloading delay and running time.

Wireless Video Sensor Networks (WVSNs) are becoming one of the most used technologies for surveillance and livestock monitoring. They are composed of small embedded video and camera motes that capture video frames periodically and send them to a specific node called a sink. Sending all the captured images to the sink consumes a lot of energy on every sensor and may cause a bottleneck at the sink level. Energy consumption and bandwidth limitation are two important challenges in WVSNs because of the limited energy of nodes and the medium scarcity. The first one is related to the sensing and transmission modules of the sensor node. The higher the frame rate and the number of frames sent, the more energy is consumed. The second one is related to the transmission module of the sensor node, the greater the number of frames sent on the network the more bandwidth is used. In this paper, FRABID, a joint data reduction, and frame rate adaptation on sensing and transmission phases mechanism is introduced. This approach reduces the number of sensed frames based on a similarity method. The aim is to adapt the number of sensed frames based on the degree of difference between two consecutive sensed frames in each period. This adaptation technique maintains the accuracy of the video while capturing frames holding new information. This approach is validated through simulations using real data-sets from video sensors [3]. The results show that the amount of sensed data is reduced by more than 70% compared to a recent algorithm in [2], while guaranteeing the detection of all the critical events at the sensor node level.

Numerous medium access control (MAC) have been proposed for Low-power Lossy Networks (LLNs) over the recent years. They aim at ensuring both energy efficiency and robustness of the communication transmissions. Nowadays, we observe deployments of LLNs for potentially critical application scenarios (e.g., plant monitoring, building automation), which require both determinism and security guarantees. They involve battery-powered devices which communicate over lossy wireless links. Radio interfaces are turned off by a node as soon as no traffic is to be sent or relayed. Denial-of-sleep attacks consist in exhausting the devices by forcing them to keep their radio on. We here focus on jamming attacks whose impact can be mitigated by approaches such as time-division and channel hopping techniques. We use the IEEE 802.15.4e standard to show that such approaches manage to be resistant to basic jamming but yet remain vulnerable to selective jamming. We discuss the potential impacts of such onslaughts, depending on the knowledge gained by the attacker, and to what extent envisioned protections may allow jamming attacks to be handled at upper layers.

Disaster scenarios are particularly devastating in urban environments, which are generally very densely populated. Disasters not only endanger the life of people, but also affect the existing communication infrastructure. In fact, such an infrastructure could be completely destroyed or damaged; even when it continues working, it suffers from high access demand to its resources within a short period of time, thereby compromising the efficiency of rescue operations. This work leverages the ubiquitous presence of wireless devices (e.g., smartphones) in urban scenarios to assist search and rescue activities following a disaster. It considers multi-interface wireless devices and drones to collect emergency messages in areas affected by natural disasters. Specifically, it proposes a collaborative data collection protocol that organizes wireless devices in multiple tiers by targeting a fair energy consumption in the whole network, thereby extending the network lifetime. Moreover, it introduces a scheme to control the path of drones so as to collect data in a short time. Simulation results in realistic settings show that the proposed solution balances the energy consumption in the network by means of efficient drone routes, thereby effectively assisting search and rescue operations.

Over the years, LoRa has been raising a lot of interest, both from the academia and the industry. LoRa is indeed a promising technology that enables long range and low power communications to connect IoT devices to the Internet. LoRa is often envisioned for several applications such as regular reporting but it is also aimed to be exploited for other applications, such as geolocation for instance. However, current implementations of LoRa geolocation system or other alternative applications suffer from severe inaccuracy. The purpose of this paper is to investigate where the inaccuracy comes from but also to determine LoRa technology based geolocation best uses-cases. The paper characterizes the LoRa radio signal properties, through a real validation campaign, over different contexts and different LoRa physical layer settings. Our results show that the signal properties, such as RSSI or SNR, are more stable in a peri-urban area than in a high density urban area. In the latter scenario, we also observe an asymmetric reception.

Long being used for identification purposes, a new set of applications is now available thanks to the development of RFID technology. One of which is remote sensing of environmental values using passive RFID tags. This leap forward allowed a more energy efficient and cheaper solution for applications like logistics or urban infrastructure monitoring. Nevertheless, serious issues raised with the use of RFID: (i) reading collisions and (ii) gathering of tag information. Indeed, tags information retrieved by readers have to be transmitted towards a base station through a multihop scheme which can interfere with neighboring readers activity. In this paper, we propose cross-layer solutions meant for both scheduling of readers' activity to avoid collisions, and a multihop routing towards base stations, to gather read tag data. This routing is performed with a data priority aware mechanism allowing end-to-end delay reduction of urgent data packets delivery up to 13% faster compared to standard ones. Using fuzzy logic, we combine several observed metrics to reduce the load of forwarding nodes and improve latency as well as data rate. We validate our proposal running simulations on industrial and urban scenarios.

In cellular networks, the outage probability is the probability that the signal-to-interference-plus-noise-ratio (SINR) is less than a given threshold. In this article, firstly, we develop an explicit formula for outage probability for a wireless sensor network taking account of the problem parameters - i.e. the spatial density of nodes in the network, the noise power, the transmission power, the distance between the considered node and the base station, and finally the attenuation coefficient -. Secondly, we model a given node by a Markov chain to study the performance of the MAC layer in the case of a Slotted-ALOHA protocol and Slotted-ALOHA with preliminary channel reservation. Especially in this study, we theoretically estimate the transmission success likelihood after a certain number of attempts that we consider as a metric to evaluate the performance of the considered protocol. Finally we compare the theoretical results with experimental ones achieved on the large scale platform FIT IoT-Lab of Lille. To sum up, this paper responds to the following question: How many channels do we need to a achieve high performance distributed wireless sensor network? The answer is given by the necessary number of channels which maximizes the transmission success likelihood after a certain number of trials.

Several wild animal species are endangered by poaching. As a solution, deploying wireless sensors on animals able to send regular messages and also alert messages has been envisaged recently by several authorities and foundations. In that context, this paper proposes WildMAC, a multichannel, multihop wireless communication protocol for these specific wireless sensor networks that have to collect data from unknown large areas with different QoS requirements. WildMAC is a TDMA based MAC protocol that leverages long range communication properties to propose an efficient data collection mean. Its performance evaluation shows it meets QoS requirements.

This paper studies the RSU deployment problem in a 2-D urban or suburban road scenario of a vehicular ad hoc network (VANET). To optimize RSU deployment, we introduce the notion of centrality in a social network to RSU deployment, and use it to measure the importance of an RSU position candidate in RSU deployment. Based on the notion of centrality, we propose a centrality-based RSU deployment approach and formulate the RSU deployment problem as a linear programming problem with the objective to maximize the total centrality of all position candidates selected for RSU deployment under the constraint of a given deployment budget. To solve the formulated problem, we analogize the problem to a 0-1 Knapsack problem and thus employ a 0-1 Knapsack algorithm to solve the problem. In the analogy, the budget in the RSU deployment problem is analogous to the bag's capacity in the Knapsack problem, the cost of deploying an RSU is analogous to an item's weight, and the centrality of a position candidate is analogous to an item's value. Simulation results show that the proposed centrality-based deployment approach can effectively improve the efficiency of the RSU deployment in terms of the coverage time ratio as compared to a random deployment approach.

Modern vehicles are equipped with several interconnected sensors on board for monitoring and diagnosis purposes; their availability is a main driver for the development of novel applications in the smart vehicle domain. In this paper, we propose a Docker container-based platform as solution for implementing customized smart car applications. Through a proof-of-concept prototype-developed on a Raspberry Pi3 board-we show that a container-based virtualization approach is not only viable but also effective and flexible in the management of several parallel processes running on On Board Unit. More specifically, the platform can take priority-based decisions by handling multiple inputs, e.g., data from the CANbus based on the OBD II codes, video from the on-board webcam, and so on. Results are promising for the development of future in-vehicle virtualized platforms.

Opportunistic communications present a promising solution as a disaster network recovery in emergency situations such as hurricanes, earthquakes and floods where infrastructure might be damaged. Recent works have proposed opportunistic-based disaster recovery solutions. However, two main features were left behind. On the one hand, these works do not consider the assortment of networks integrated in mobile devices (e.g. WiFi-Direct, WiFi ad-hoc, bluetooth). Moreover, they do not consider mobile devices that come with various energy levels. This paper proposes COPE, a cooperative opportunistic alert diffusion approach for disaster scenario useful for trapped survivors. COPE considers mobile devices equipped with multiple network interfaces and have various battery power levels. In order to maintain mobile devices alive longer, survivors form cliques and zones in which they diffuse alternately and periodically alert messages until reaching a potential rescuers. Simulation results show that COPE largely outperforms the selfish diffusion scheme in terms of energy consumption while guaranteeing an important alert delivery success.

The popularization of RFID systems has conducted to large deployments of RFID solutions in various areas under different criteria. However, such deployments, specially in dense environments, can be subject to RFID collisions which in turn affect the quality of readings. In this paper we propose two distributed and efficient solutions for dense mobile deployments of RFID systems. mDEFAR is an adaptation of a previous work highly performing in terms of collisions reduction, efficiency and fairness in dense static deployments. CORA is more of a locally mutual solution where each reader relies on its neighborhood to enable itself or not. Using a beaconing mechanism, each reader is able to identify potential (non-)colliding neighbors in a running frame and as such chooses to read or not. Performance evaluation shows high performance in terms of coverage delay for both proposals quickly achieving 100% coverage depending on the considered use case while always maintaining consistent efficiency levels above 70%. Compared to GDRA, our solutions proved to be better suited for highly dense and mobile environments, offering both higher throughput and efficiency. The results reveal that depending on the application considered, choosing either mDEFAR or CORA helps improve efficiency and coverage delay.

Cognitive radio technology is a promising solution to the exponential growth in bandwidth demand sustained by increasing number of ubiquitous connected devices. The allocated spectrum is opened to the secondary users conditioned on limited interference on the primary owner of the band. A major bottleneck in cognitive radio systems is to find the best available channel quickly from a large accessible set of channels. This work formulates the channel exploration-exploitation dilemma as a multi-arm bandit problem. Existing theoretical solutions to a multi-arm bandit are adapted for cognitive radio and evaluated in an experimental test-bed. It is shown that a Thompson sampling based algorithm efficiently converges to the best channel faster than the existing algorithms and achieves higher asymptotic average throughput. We then propose a multihop extension together with an experimental proof of concept.

Among all the Internet of Things (IoT) applications, the Smart City concept has received significant attention in the last few years. The main motivation behind this interest is attributable to population growth and urbanization trend. Cities need, indeed, to be ready to face new challenges - e.g., traffic congestion, wast management, etc. - caused by this new amount of citizens. To address those issues, many IoT solutions have already been proposed and many others are still under investigation; anyway, those initiatives are all based on different standards and protocols, while the Smart City concept requires integration among all the stakeholders. In this paper, we present the VITAL architecture, which aims to integrate Internet- Connected Objects (ICOs) among multiple IoT platforms and ecosystems. In particular, we introduce the "ICOs and Services Discovery" module, which makes completely transparent, for users, the exploration of data-sources that are appropriate for his/her business context. This mechanism is at the basis of the Cloud of Things paradigm and a key feature as the platform agnostic property is an essential goal for VITAL.

RFID technology suffers from a recurring issue: the reader-to-reader collision. Numerous protocols have been proposed to attempt to reduce them, but, remaining reading errors still heavily impact the performances and fairness of dense RFID deployments. This paper introduces a new Distributed Efficient & Fair Anticollision for RFID (DEFAR) protocol. It reduces both monochannel and multichannel collisions as well as interference by a factor of almost 90% in comparison with the best state of the art protocols. The fairness of the medium access among the readers is improved to a 99% level. Such improvements are achieved applying a TDMA-based “serverless” approach and assigning different priorities to readers depending on their behavior over precedent rounds. A distributed reservation phase is organized between readers with at least one winning reader afterwards. Then, multiple reading phases occur within a single frame in order to obtain fast coverage and high throughput. The use of different reader priorities based on reading behaviors of previous frames also contributes to improve both fairness and efficiency. Simulation results show the robustness of the proposed solution in terms of different metrics such collision avoidance, fairness and coverage and in comparison with a centralized literature solution.

In the era of Internet of Things (IoT), the development of Wireless Sensor Networks (WSN) arises different challenges. Two of the main issues are electromagnetic interference and the lifetime of WSN nodes. In this paper, we show and evaluate experimentally the relation between interference and energy consumption, which impacts the network lifetime. We present a platform based on commercially available low-cost hardware in order to evaluate the impact of electromagnetic interference in 2.4 GHz ISM band on energy consumption of WSN. The energy measurements are obtained separately from each electronic component in the node. Interference and energy measurements are conducted in an anechoic chamber and in an office-type lab environment. X-MAC protocol is chosen to manage the Radio Duty Cycle of the nodes and its energy performance is evaluated. The energy consumption transmitter nodes is analyzed particularly in this work. Moreover, this energy consumption has been quantified and differentiated according to the number of (re-)transmissions carried out by the transmitter as well as the number of ACK packets sent by the receiver for a single packet. Finally, we use a model of real battery to calculate the lifetime of the node for operation within different interference level zones. This study lays the basis for further design rules of communication protocols and development of WSNs.

With the development of new technologies, these last years have witnessed the emergence of a new paradigm: the Internet of Things (IoT) and of the physical world. We are now able to communicate and interact with our surrounding environment through the use of multiple tiny sensors, RFID technologies or small wireless robots. This allows a set of new applications and usages to be envisioned ranging from logistic and traceability purposes to emergency and rescue operations going through the monitoring of volcanos or forest fires. However, all this comes with several technical and scientific issues like how to ensure the reliability of wireless communications in disturbed environments, how to manage efficiently the low resources (energy, memory, etc) or how to set a safe and sustainable maintenance. All these issues are addressed by researchers all around the world but solutions designed for IoT need to face real experimentations to be validated. To ease such experimentations for IoT, several experimental test beds have been deployed offering diverse and heterogeneous services and tools. This article studies the different requirements and features such facilities should offer and survey the different experimental facilities currently available for the community, the different hardware used (as sensors and robots) and the scope of their services. We expect this survey assist a potential user to easily choose the one to use regarding his own needs. Finally, we identify existing gaps and difficulties and investigate new directions for such facilities.

This paper analyzes the information delivery delay for roadside unit deployment in an intermittently connected vehicular network. An analytical model is developed to describe the relationship between the average information delivery delay and the distance between two neighbor RSUs deployed along a road. The derived model considers a straight highway scenario where two RSUs are deployed at a distance without any direct connection and vehicles are sparsely distributed on the road with road condition information randomly generated between the two neighbor RSUs. Moreover, the model takes into account the vehicle speed, the vehicle density, the likelihood of an incident, and the distance between two RSUs. The effectiveness of the derived analytical model is verified through simulation results. Given the delay requirement of some time-critical applications, this model can be used to estimate the maximum distance allowed between two neighbor RSUs, which can provide a reference basis for the deployment of RSUs in such scenarios.

Broadcasting is an effective routing paradigm for data dissemination in vehicular ad hoc networks (VANETs). One concern that arises with broadcasting is the broadcast storm problem, which would cause node contentions and data collisions, and thus degrade the transmission efficiency of a network. This paper proposes a Dynamic trAnsmission delaY based broadcast (DAYcast) protocol for a VANET. To alleviate the effect of the broadcast storm and improve the transmission efficiency of the network, DAYcast only allows the effective neighbors of a source vehicle to broadcast a received data packet and the selection of the effective neighbors are based on the position information on the one-hop neighbors of the source vehicle. Meanwhile, it allows each effective neighbor to wait a certain transmission delay before it broadcasts a received packet. The transmission delay of an effective neighbor depends on the distance between the neighbor and the source vehicle, and the number of effective neighbors of the source vehicle. Simulation results show that DAYcast can effectively improve the network performance in terms of network reachability and the successful delivery ratio as compared with existing weighted p-persistence broadcasting (WPB) and slotted 1-persistence broadcasting (SPB).

This paper focuses on the coverage of specific Zones of Interest that can change dynamically over time by using a swarm of flying robots. The mobility of the flying devices is achieved by the design of two distributed and local algorithms. The first algorithm is based on Particle Swarm Optimization (PSO) and Virtual Forces Algorithm (VFA). We modify the classical PSO approach to propose a totally distributed algorithm, which only requires the flying robots to receive local information from the neighbors to update their velocity and trajectory (PSO-S). This new distributed version of the PSO is combined with a distributed version of the Virtual Forces Algorithm. The second algorithm is a distributed implementation of the VFA (VFA-D). To the best of our knowledge, these two approaches are novel in their distributed character, scalability and implementability on resource-constrained devices. We show that the proposed algorithms are reactive, i.e. able to capture in an effective fashion the events happening within the field even if the position of the events changes over time. To show the effectiveness of the proposed techniques, we perform extensive simulations to compare both the PSO-S and the VFA-D schemes with a centralized version of the VFA. Simulations show the good performance in terms of coverage and traveled distance as well as the high reactivity of both PSO-S and VFA-D when the ZoI changes.

According to a wide range of studies, IT should become a key facilitator in establishing primary education, reducing mortality and supporting commercial initiatives in Least Developed Countries (LDCs). The main barrier to the development of IT services in these regions is not only the lack of communication facilities, but also the lack of consistent information systems, security procedures, economic and legal support, as well as political commitment. In this paper, we propose the vision of an infrastructureless data platform well suited for the development of innovative IT services in LDCs. We propose a participatory approach, where each individual implements a small subset of a complete information system thanks to highly secure, portable and low-cost personal devices as well as opportunistic networking, without the need of any form of infrastructure. We review the technical challenges that are specific to this approach.

In this paper we focus on the problem of dynamic event coverage. We assume that no knowledge about either event position or duration is given a priori. Nonetheless, the events need to be monitored and covered thanks to mobile wireless sensors. Thus, mobile sensors have to discover the events and move towards a new Zone of Interest (ZoI) when the previous monitored event is over. An efficient, distributed and localized solution of this problem would be immediately exploitable by several applications domains, such as environmental, civil, etc. We propose two novel approaches to deal with dynamic event coverage. The first one is a modified version of the PSO, where particles (mobile sensors, nodes or devices in the following) update their velocity by using only local information coming from their neighbors. In practice, the velocity update is performed by considering neighbors' sensed events. Our distributed version of PSO is integrated with a distributed version of the Virtual Force Algorithm (VFA). Virtual Force approach has the ability to “position” nodes with no overlap, by using attractive and repulsive forces based on the distance between nodes. The other proposed algorithm is a distributed implementation of the VFA by itself. Both techniques are able to reach high levels of coverage and show a satisfying reactivity when the ZoI changes. This output parameter is measured as the capability for the sensors to “follow” a sequence of events happening in different ZoIs. The effectiveness of our techniques is shown through a series of simulations and comparisons with the classical centralized VFA.

Routing is a critical issue in vehicular ad hoc networks (VANETs). This paper considers the routing issue in both vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications in VANETs, and proposes a Moving dirEction and DestinAtion Location based routing (MEDAL) algorithm for supporting V2V and V2I communications. MEDAL takes advantage of both the moving directions of vehicles and the destination location to select a neighbor vehicle as the next hop for forwarding data. Unlike most existing routing algorithms, it only uses a HELLO message to obtain or update routing information without using other control messages, which largely reduces the number of control messages used in routing. Simulation results show that MEDAL can significantly improve the packet delivery ratio of the network as compared with the well-known Ad hoc On-demand Distance Vector Routing (AODV) algorithm.

HELLO protocol or neighborhood discovery is essential in wireless ad hoc networks. It makes the rules for nodes to claim their existence/aliveness. In the presence of node mobility, no fix optimal HELLO frequency and optimal transmission range exist to maintain accurate neighborhood tables while reducing the energy consumption and bandwidth occupation. Thus a Turnover based Frequency and transmission Power Adaptation algorithm (TFPA) is presented in this paper. The method enables nodes in mobile networks to dynamically adjust both their HELLO frequency and transmission range depending on the relative speed. In TFPA, each node monitors its neighborhood table to count new neighbors and calculate the turnover ratio. The relationship between relative speed and turnover ratio is formulated and optimal transmission range is derived according to battery consumption model to minimize the overall transmission energy. By taking advantage of the theoretical analysis, the HELLO frequency is adapted dynamically in conjunction with the transmission range to maintain accurate neighborhood table and to allow important energy savings. The algorithm is simulated and compared to other state-of-the-art algorithms. The experimental results demonstrate that the TFPA algorithm obtains high neighborhood accuracy with low HELLO frequency (at least 11% average reduction) and with the lowest energy consumption. Besides, the TFPA algorithm does not require any additional GPS-like device to estimate the relative speed for each node, hence the hardware cost is reduced.

Clustering in wireless sensor networks is an efficient way to structure and organize the network. It aims to identify a subset of nodes within the network and bind it a leader (i.e. cluster-head). This latter becomes in charge of specific additional tasks like gathering data from all nodes in its cluster and sending them by using a longer range communication to a sink. As a consequence, a cluster-head exhausts its battery more quickly than regular nodes. In this paper, we present BLAC, a novel Battery-Level Aware Clustering family of schemes. BLAC considers the battery-level combined with another metric to elect the cluster-head. It comes in four variants. The cluster-head role is taken alternately by each node to balance energy consumption. Due to the local nature of the algorithms, keeping the network stable is easier. BLAC aims to maximize the time with all nodes alive to satisfy application requirements. Simulation results show that BLAC improves the full network lifetime 3-time more than traditional clustering schemes by balancing energy consumption over nodes and still delivering high data percentage.

Controlled mobility is one of the most complex challenges in Wireless Sensor Networks (WSN). Only a few routing protocols consider controlled mobility in order to extend the network lifetime. They are all designed to optimize the physical route topology from a source to a destination. However, there is often more than one sensor which reports an event to the sink in WSN. In existing solutions, this leads to oscillation of nodes which belong to different routes and their premature death. Experiments show that the need of a routing path merge solution is high. As a response we propose the first routing protocol which locates and uses paths crossing to adapt the topology to the network traffic in a fully localized way while still optimizing energy efficiency. Furthermore the protocol makes the intersection to move away from the destination, getting closer to the sources, allowing higher data aggregation and energy saving. Our approach outperforms existing solutions and extends network lifetime up to 37%.

Mitigating reader-to-reader collisions is one of the principal challenges in a large-scale dynamic RFID system with a number of readers deployed in order to maximize the system performance (i.e., throughput, fairness and latency). In prior works, contention-based and activity scheduling medium access control (MAC) protocols are commonly used approaches to reduce such problems. Existing protocols typically perform worse in a large-scale RFID dynamic system and require more additional components or are based on unrealistic assumptions. So far, many research efforts have been made to improve the performance or the reliability of Carrier Sense Multiple Access (CSMA) techniques for Mobile Ad-Hoc Networks (MANETs) by using an adaptive Backoff schemes. In this paper, we look at these well known solutions that proved their efficiency in high congestion wireless networks. We evaluate the performance and characterize these solutions when they are used to reserve the wireless channel through broadcasting message for reader-to-tag communication. Based on the application requirements, we study their capacity to mitigate collisions, the channel access latency, the average number of successful requests sent per reader and the fairness index in the context of RFID networks.

We propose a radically new family of geometric graphs, i.e., Hypocomb (HC), Reduced Hypocomb (RHC), and Local Hypocomb (LHC). HC and RHC are extracted from a complete graph; LHC is extracted from a Unit Disk Graph (UDG). We analytically study their properties including connectivity, planarity, and degree bound. All these graphs are connected (provided that the original graph is connected) planar. Hypocomb has unbounded degree while Reduced Hypocomb and Local Hypocomb have maximum degree 6 and 8, respectively. To our knowledge, Local Hypocomb is the first strictly localized, degree-bounded planar graph computed using merely 1-hop neighbor position information. We present a construction algorithm for these graphs and analyze its time complexity. Hypocomb family graphs are promising for wireless ad hoc networking. We report our numerical results on their average degree and their impact on FACE routing. We discuss their potential applications and pinpoint some interesting open problems for future research.

We propose to apply the concept of Cost-to-Progress Ratio (CPR) in greedy routing for load reduction and balancing. The load of a node is the percentage of time it is occupied by forwarding traffic or inability to forward due to interference. The resultant routing protocol, named CPR-routing, is a localized parameterless approach, optimizing the ratio of nodal load and geographic progress. Through extensive simulation, we evaluate it in comparison with an existing parameter-based localized solution, α-routing. Our simulation results indicate that CPR-routing outperforms α-routing in per node load, success rate, and average hop count.

Neighborhood discovery - detection of the devices within communication range using hello messages - is a fundamental mechanism in wireless sensor networks (WSN) that enables usage of many different topology control and routing algorithms. In this work, we combine parameters of the neighborhood discovery (sending frequency of hello messages and changes in the neighborhood tables) and transmission range of the nodes. We present two algorithms that adapt transmission range of the sensors in a mobile WSN by still adapting frequency of hello messages in order to save energy and get accurate neighborhood tables. The first solution is based on the knowledge of turnover - change in the number of neighbors in consecutive iterations of the neighborhood discovery - used in conjunction with an adaptation of the message frequency and the transmission range, minimizing overall transmission cost of hello messages. The second solution is based on the computation of optimal range knowing the nodes' speed. Both algorithms are based on theoretical analysis. Results show that they are energy efficient and outperform solutions of the literature by maintaining high accuracy.

In mobile-beacon assisted sensor localization, beacon mobility scheduling aims to determine the best beacon trajectory so that each sensor receives sufficient beacon signals and becomes localized with minimum delay. We propose a novel DeteRministic dynamic bEAcon Mobility Scheduling (DREAMS) algorithm, without requiring any prior knowledge of the sensory field. In this algorithm, the beacon trajectory is defined as the track of Depth-First Traversal (DFT) of the network graph, thus deterministic. The mobile beacon performs DFT dynamically, under the instruction of nearby sensors on the fly. It moves from sensor to sensor in an intelligent heuristic manner according to Received Signal Strength (RSS)-based distance measurements. We prove that DREAMS guarantees full localization (every sensor is localized) when the measurements are noise-free, and derive the upper bound of beacon total moving distance in this case. Then, we suggest to apply node elimination and Local Minimum Spanning Tree (LMST) to shorten beacon tour and reduce delay. Further, we extend DREAMS to multibeacon scenarios. Beacons with different coordinate systems compete for localizing sensors. Loser beacons agree on winner beacons' coordinate system, and become cooperative in subsequent localization. All sensors are finally localized in a commonly agreed coordinate systems. Through simulation we show that DREAMS guarantees full localization even with noisy distance measurements. We evaluate its performance on localization delay and communication overhead in comparison with a previously proposed static path-based scheduling method.

We propose a radically new family of geometric graphs, i.e., Hypocomb, Reduced Hypocomb and Local Hypocomb. The first two are extracted from a complete graph; the last is extracted from a Unit Disk Graph (UDG). We analytically study their properties including connectivity, planarity and degree bound. All these graphs are connected (provided the original graph is connected) planar. Hypocomb has unbounded degree while Reduced Hypocomb and Local Hypocomb have maximum degree 6 and 8, respectively. To our knowledge, Local Hypocomb is the first strictly-localized, degree-bounded planar graph computed using merely 1-hop neighbor position information. We present a construction algorithm for these graphs and analyze its time complexity. Hypocomb family graphs are promising for wireless ad hoc networking. We report our numerical results on their average degree and their impact on FACE routing. We discuss their potential applications and some open problems.

In mobile-beacon assisted sensor localization, beacon mobility scheduling aims to determine the best beacon trajectory so that each sensor receives sufficient beacon signals with minimum delay. We propose a novel DeteRministic bEAcon Mobility Scheduling (DREAMS) algorithm, without requiring any prior knowledge of the sensory field. In this algorithm, beacon trajectory is defined as the track of depth-first traversal (DFT) of the network graph, thus deterministic. The mobile beacon performs DFT under the instruction of nearby sensors on the fly. It moves from sensor to sensor in an intelligent heuristic manner according to RSS (Received Signal Strength)-based distance measurements. We prove that DREAMS guarantees full localization (every sensor is localized) when the measurements are noise-free. Then we suggest to apply node elimination and topology control (Local Minimum Spanning Tree) to shorten beacon tour and reduce delay. Through simulation we show that DREAMS guarantees full localization even with noisy distance measurements. We evaluate its performance on localization delay and communication overhead in comparison with a previously proposed static path based scheduling method.

A vehicular ad hoc network (VANET) is a type of mobile network whose nodes are traveling cars and which communicate with one another using short-range wireless communications. These cars can exchange and share different information among them, which can lead to the development of interesting applications that require the cooperation of vehicles or using the vehicular network as a distributed computing platform. Along with the opportunities offered by vehicular networks, a number of challenges also arise. In this paper, we focus on information gathering in vehicular ad hoc networks by using a pull model. Such model would allow users to send queries to a set of cars in order to find the desired information. In order to propose such a query processing scheme, the main challenge to address is how to route the different results towards their recipient in a highly dynamic network where the nodes move very quickly. To solve this, we propose GeoVanet, a DHT-based geographic routing protocol which ensures that the sender of a query can get a consistent answer. Our goal is not to compute the query result "instantaneously" but to ensure that the user will be able to retrieve it within a bounded time. To prove the effectiveness of GeoVanet, an experimental evaluation is provided, which shows that up to 80% of the available query results are delivered to the user.

This paper presents a description of SensLAB(Very Large Scale Open Wireless Sensor Network Testbed) that has been developed and deployed in order to allow the evaluation through experimentations of scalable wireless sensor network protocols and applications. SensLAB’s main and most important goal is to offer an accurate open access multi-users scientific tool to support the design, the development tuning, and the experimentation of real large-scale sensor network applications. The SensLAB testbed is composed of 1024 nodes over 4 sites. Each site hosts 256 sensor nodes with specific characteristics in order to offer a wide spectrum of possibilities and heterogeneity. Within a given site, each one of the 256 nodes is able both to communicate via its radio interface to its neighbors and to be configured as a sink node to exchange data with any other ” sink node ”. The hardware and software architectures that allow to reserve, configure, deploy firmwares and gather experimental data and monitoring information are described. We also present demonstration examples to illustrate the use of the SensLAB testbed and encourage researchers to test and benchmark their applications/protocols on a large scale WSN testbed.

SensLAB's goal is to provide a very large scale open wireless sensor network testbed, by deploying 1024 nodes over 4 interconnected sites, and to offer a reference tool for the wireless sensor network community. Our demonstration purpose is to illustrate what the SensLAB1platform offers through a case study: an animal tracking application needing routing and mobile node geolocalization. Through this example, we will show how to run an application in the large scale testbed SensLAB and some of the tools it provides to help in developing, tuning and monitoring such a large scale application.

Some current and future pervasive data driven applications must operate in “extreme” environments where end-to-end connectivity cannot be guaranteed at all times. In fact, it is likely that in these environments partitions are, rather than exceptions, part of the normal network operation. In this paper, we introduce Cover, a suite of adaptive strategies to control the trajectory of “infrastructure” nodes, which are deployed to bridge network partitions and thus play a critical role in data delivery. In particular, we focus on applications where end (or target) nodes are mobile and their mobility is unknown. Our goal is then to deploy and manage infrastructure nodes so that application-level requirements such as reliable data delivery and latency are met while still limiting deployment cost and balancing the load among infrastructure nodes. Cover achieves these goals using a localized and adaptive approach to infrastructure management based on the observed mobility of target nodes. To this end, Cover takes advantage of contact opportunities between infrastructure nodes to exchange information about their covered zones, and thus, help monitor targets in a more efficient fashion. Through extensive simulations, we show how Cover's adaptive features yield a fair distribution of targets per infrastructure node based only on limited network knowledge.

In the anycasting problem, a sensor wants to report event information to one of sinks or actors. We describe the first localized anycasting algorithms that guarantee delivery for connected multi-sink sensor-actor networks. Let S(x) be the closest actor/sink to sensor x, and |xS(x)| be distance between them. In greedy phase, a node s forwards the packet to its neighbor v that minimizes the ratio of cost cost(|sv|) of sending packet to v (here we specifically apply hop-count and power consumption metrics) over the reduction in distance (|sS(s)|-|vS(v)|) to the closest actor/sink. A variant is to forward to the first neighbor on the shortest weighted path toward v. If none of neighbors reduces that distance then recovery mode is invoked. It is done by face traversal toward the nearest connected actor/sink, where edges are replaced by paths optimizing given cost. A hop count based and two variants of localized power aware anycasting algorithms are described. We prove guaranteed delivery property analytically and experimentally.

We propose a localized sensor localization scheme making full use of controlled mobility of a location-aware actor and the connectivity of the sensor network. It contains two new algorithms: a unscented particle filter (UPF) based localization algorithm and an actor mobility scheduling algorithm. The former is an application of UPF. It enables sensor self-localization using received signal strength indicator and actor position. The latter models actor mobility scheduling as traveling salesman problem and aims at fully localized network and minimized time delay. Navigated by sensors, the actor depth-first traverses a local minimum spanning tree of a connected 3-dominating set of the network.

Existing theoretical and simulation studies on georouting appear detached from experimental studies in real environments. We set up our test environment by using WSN430 wireless sensor nodes. To overcome the need for significant number of wireless nodes required to perform a realistic experiment in high density network, we introduce a novel approach - emulation by using relatively small number of nodes in 1-hop experimental setup. Source node is a fixed sensor, all available sensors are candidate forwarding neighbors with virtual destination. Source node makes one forwarding step, destination position is adjusted, and the same source again searches for best forwarder. We compare three georouting algorithms. We introduce here greedy geographical routing algorithms in a real environment (GARE) which builds a RNG by using ETX(uv)/|uv| as edge weight (ETX(uv) counts all transmissions and possibly acknowledgments between two nodes until message is received), and selects RNG neighbor with greatest progress toward destination (if none of RNG neighbors has progress, all neighbors are considered). Our experiments show that GARE is significantly more efficient than existing XTC algorithm (applying RNG on ETX(uv)) in energy consumption. COP GARE selects neighbor with progress that minimizes ETX(uv)/|uv|, and outperforms both algorithms.

This paper considers the problem of designing power efficient routing with guaranteed delivery for sensor networks with known distances between neighbors but unknown geographic locations. We propose Hector, a hybrid energy efficient tree-based optimized routing protocol, based on two sets of virtual coordinates. One set is based on rooted tree coordinates, and the other is based on hop distances toward several landmarks. In our algorithm, the node currently holding the packet forwards it to its neighbor that optimizes ratio of power cost over distance progress with landmark coordinates, among nodes that reduce landmark coordinates and do not increase tree coordinates. If such a node does not exist then forwarding is made to the neighbor that reduces tree based distance and optimizes power cost over tree distance progress ratio. Our simulations show the superiority of our algorithm over existing alternatives while guaranteeing delivery, and only up to 30% additional power compared to centralized shortest weighted path algorithm.

We propose a novel localized routing protocol for wireless sensor networks (WSN) that is energy-efficient and guarantees delivery. We prove that it is constant factor of the optimum for dense networks. To forward a packet, a node s in graph G computes the cost of the energy weighted shortest path (SP) between s and each of its neighbors which are closer to the destination than itself. It then selects node x which minimizes the ratio of the cost of the SP to the progress towards the destination. It then sends the message to the first node on the SP from s to x: say node x'. Node x' restarts the same greedy routing process until the destination is reached or the routing fails. To recover from failure, our algorithm invokes face routing that guarantees delivery. This work is the first to optimize energy consumption of face routing. First, we build a connected dominating set from graph G, second we compute its Gabriel graph to obtain the planar graph G'. Face routing is applied on G' only to decide which edges to follow in the recovery process. On each edge, greedy routing is used. This two-phase (greedy-Face) end-to-end routing process (EtE) reiterates until the final destination is reached. Simulation results show that EtE outperforms several existing geographical routing on energy consumption metric.

We present a turnover based adaptive HELLO protocol (TAP), which enables nodes in mobile networks to dynamically adjust their HELLO messages frequency depending on the current speed of nodes. To the best of our knowledge, all existing solutions are based on specific assumptions (e.g., slotted networks) and/or require specific hardware (e.g., GPS) for speed evaluation. One of the key aspects of our solution is that no additional hardware is required since it does not need this speed information. TAP may be used in any kind of mobile networks that rely on HELLO messages to maintain neighborhood tables and is thus highly relevant in the context of ad hoc and sensor networks. In our solution, each node has to monitor its neighborhood table to count new neighbors whenever a HELLO is sent. This turnover is then used to adjust HELLO frequency. To evaluate our solution, we propose a theoretical analysis based on some given assumptions that provides the optimal turnover when these assumptions hold. Our experimental results demonstrate that when this optimal value is used as the targeted turnover in TAP, the HELLO frequency is correctly adjusted and provides a good accuracy with regards to the neighborhood tables.

We propose an energy efficient routing protocol, VCost, for sensor networks. We assume that nodes are unaware of their geographic location thus, VCost assigns virtual coordinates to nodes as follows. Based on the node hop count distances from a set of landmarks, our method computes a distance metric to obtain the node's virtual coordinates. VCost, then uses these coordinates to route packets from node u to node v, in its neighborhood, such that the ratio of the cost to send a message to v to the progress in the routing task towards the destination is minimized. Compared to existing algorithms that use virtual locations, our simulation shows that VCost improves significantly energy consumption and preserves the small percentage of successful routings.