With the rapid expansion of Internet of Things (IoT) devices, current LPWAN technologies are increasingly susceptible to performance deterioration. LoRaWAN stands out as a leading LPWAN solution due to its ability to support long-range, low-power communication in massive IoT deployments. However, ensuring data reliability in dense and dynamic environments while optimizing transmission settings poses significant challenges, particularly in terms of interference mitigation and energy efficiency. To overcome these limitations, this paper proposes an innovative approach that integrates K-means clustering and leverages reinforcement learning to effectively manage gateway placement and configure transmission parameters, such as spreading factors and power levels, based on local signal conditions and environmental feedback. The model explicitly accounts for the capture effect and the imperfect orthogonality of spreading factors, allowing for more realistic and robust adaptation strategies without relying on the conventional centralized Adaptive Data Rate (ADR) mechanism. The approach is validated through a real-world experimental deployment. Furthermore, we highlighted the novelty of our approach by a detailed analysis of LoRaWAN spreading factor (SF) sensitivity thresholds, and evaluated the impact of gateway densification and SF reallocation on network performance. Both experimental and simulation outcomes show significant improvements in data extraction rate (DER) and energy efficiency, particularly in large-scale, congested network scenarios.

Edge computing architectures have greatly benefited from high-performance end systems and new-generation cellular networks. Smart cities now have the potential to synthesize all the aspects of a heterogeneous communication and computing infrastructure, leveraging high-definition sensors to propose multiple applications to a variety of road users. However, with the advent of connected vehicles and due to the short lifetime of OBU-to-RSU links, the issue of mobility threatens the reliability of task and data offloading. We thus propose a multi-RAT data offloading scheme exploiting device-to-device communications. In particular, considering the large amounts of data generated by an on-board lidar, we establish a bilateral offloading scheme where the RSU hosts a management algorithm. Based on this algorithm, different packet sizes may be requested based on the networking performance of each vehicle. Through extensive field trials and simulations, performance evaluation shows clear performance discrepancies between C-V2X and DSRC, with C-V2X being more appropriate for dynamically-managed offloading. 4192-byte packets sent at a 10 ms interval achieve the highest throughput with 96 % delivery rates. The proposed algorithm then achieves 30-to-45% increases in throughput when compared to an OBUcentered scheme due to improved resource allocation.

Vehicular connectivity is becoming an integral part of the automotive industry, pushed forth by edge computing and smart cities. However, in urban environments, wireless links are prone to physical disturbances. To maintain their service with these conditions, vehicular networking architectures need the ability to anticipate phenomena and adapt proactively. The multitude of variables involved in this problem calls for deep learning approaches. As such, predictive Quality-of-Service has shown promising contributions to the performance of low-latency infrastructures, but a field study of appropriate recurrent neural networks has yet to be conducted for vehicular networks. Moreover, the inherent heterogeneity of urban networks can be leveraged for service continuity purposes, by developing a radio access technology selection algorithm based on the predictions made for each technology. In that regard, while standalone 5G has been publicized as a key technology for very demanding applications like tele-operated driving, previous works have not studied its contribution in a real-life environment. This work thereby proposes a predictive Quality-of-Service infrastructure with proactive radio access technology selection for urban vehicular networks, evaluated by field trial and involving 5G SA, C-V2X and ITS-G5. Experimental observations show the enhanced contribution of 5G SA, with a minimal latency of 16 milliseconds and higher overall reliability than device-to-device communications. These measurements pave the way to cellular vehicular communications in applications with high service level requirements. Moreover, our system is able to ensure 99.9% success rates from 72% of the vehicle travel time to more than 90% of the time. Average reliability increases from 95.7% to 99.4%.

LoRaWAN has emerged as a leading solution for supporting long-range, low-power communication in massive IoT deployments. However, ensuring reliable data transmission in dense and dynamic environments poses significant challenges, particularly with interference mitigation and energy efficiency. To address these limitations, this paper proposes an innovative approach that integrates reinforcement learning for decentralized and adaptive resource allocation. The approach enables end devices to autonomously configure their transmission parameters, such as spreading factors and power levels, based on local environmental feedback. By eliminating reliance on centralized Adaptive Data Rate mechanisms, the proposed approach significantly enhances energy efficiency, and improves scalability in high-traffic LoRaWAN networks. The approach is validated through real-world experimental deployments along with extensive simulations, demonstrating notable improvements in energy effectiveness. This work highlights the potential of reinforcement learning to optimize resources management and enhance the communication robustness in LoRaWAN environments.

The future of smart cities depends on the ability of devices to share contextual data in a quick and reliable manner. Deploying access points in close proximity to the side of the road greatly reduces the propagation distances of messages sent over the sidelink, achieving extremely low latencies. However, in challenging situations encountered in urban contexts, multiple obstacles hinder the availability of direct links between a device and its destination. While the usual fallback solution resides in cellular connectivity, the gap in performance is immense. We propose relaying packets sent using Cellular-V2X (C-V2X) sidelink, where the initial message is received by an intermediary with better connectivity to the targeted access point, then retransmitted from this new position. To explore the performance and reliability of such communications in urban V2X architectures, we present experimental campaigns exploiting C-V2X PC5, 5G, autonomous vehicles and state-of-the-art hardware. We study multiple scenarios, extending or shortening line-of-sight situations to analyze coverage and performance. Results analysis highlights high reliability of relayed C-V2X packets at up to 200-meter ranges from one peer to another, with observed latencies being, at worst, 42% more favorable than the cellular fallback.

Internet of Things (IoT) infrastructures can undoubtedly derive significant benefits from content-centric networking (CCN). Thus, a flurry of research focuses on their integration for better content retrieval. Despite the plethora of caching strategies available today, none stands out by efficiently taking into account the limitations of sensors and IoT systems such as low data-rate and energy, as well as limited memory and processing capability. In this work, we focus our attention on the effectiveness of caching on IoT edge gateways. To this end, we propose a novel Deep Reinforcement Learning (DRL) strategy that establishes an intelligent placement of contents and promotes the consideration of content popularity as well as the diversity of data in the network. Through extensive experiments and a real-world testbed on our campus, we perform an in-depth comparative analysis. We demonstrate that our approach decreases the data retrieval distance, assuredly leading to energy efficiency and reduced latency. Moreover, our DRL-based solution outperforms other strategies in terms of cache hit, content diversity, and content popularity support.

International audience

To enhance road safety and traffic fluidity, vehicles need to communicate amongst themselves and transfer large amounts of data to the infrastructure with extremely low latencies, through access points deployed closely to the road. However, in obstacle-heavy scenarios, latency increases when devices shift coverage areas. Though networks are heterogeneous by nature in the smart city era, very few mechanisms are provided in connected vehicles for multiple Radio Access Technology (multiRAT) selection. We thus present a networking approach to improve link stability, exploiting mobile and V2X technologies: LTE, 5G, Cellular-V2X, DSRC. Unlike opportunistic handover schemes taking advantage of Device-to-Device (D2D) communications when coverage is detected, we develop a proactive scheme leveraging the performance of received packets, to avoid link failure against pathloss and shadowing. An autonomous shuttle roams the playground, filling grids associating positions with QoS indicators. The grids then provide knowledge on RAT performance to incoming vehicles, facilitating selection according to position. Experimental studies show high performance of DSRC in short range, line-of-sight situations, while C-V2X offers up to 60% longer D2D ranges and more resilient coverage. Field trial analysis of our solution highlights a 27% reduction of high-latency packets, by extending V2I communications and limiting reliance on cellular connectivity.

Locating at-risk workers in hospitals using legacy private 5G networks is a daunting task that involves solving the problem of indoor localization using commercial off-the-shelf proprietary hardware. Currently, no full-stack schemes or realistic indoor positioning experiments have been conducted using 5G. In this study, we present the first comprehensive 5G framework that combines fingerprinting with the 3GPP Enhanced Cell ID (E-CID) approach. Our methodology consists of a machine-learning model to deduce the user's position by comparing the signal strength received from the User Equipment (UE) with a reference radio power map. This challenging method has four main contributions. First, the 3GPP protocols and functions are extended to provide open, secure, and universal core network-based localization functions. Second, to generate a reference map, the first paradigm of Optical Radio Power Estimation using Light Analysis (ORPELA) is introduced. Real-world experiments prove that it is reproducible and more accurate than state-of-the-art radio-planning software. Third, machine-learning models are designed, trained, and optimized for an ultra-challenging radio context. Finally, an extensive experimental campaign is conducted to demonstrate the expected indoor localization performance of realistic 5G private networks.

This paper proposes a multi-RAT Software Defined Network (SDN) assisted collision detection system designed to protect Vulnerable Road Users (VRU) by predicting road hazards. The system benefits from the Network Function Virtualization (NFV) and Software Defined Network (SDN). It includes a collision detection service, which collects specific messages from road users and processes them to detect collision risks. We propose a solution that allows vehicles and VRUs (such as pedestrians, cyclists, etc.) to smartly adapt the rate of the awareness messages. The frequency adaptation is performed under a certain number of constraints related to the risk level with the aim to improve trajectory prediction accuracy while decreasing unnecessary signalization on the network side and optimizing the energy consumption for the VRU side. Our simulation results suggest that frequency adaptation helps improve energy consumption and network signalization without impacting trajectory prediction accuracy and collision detection reliability.

Today, LoRaWAN networks have reached a major milestone on the Internet of Things application domains thanks to their fascinating characteristics, especially in terms of large coverage and low energy consumption. Several studies have focused on the performance optimization of these networks. Indeed, gateway-centric allocation strategies look for the optimal configuration of the transmission parameters and send them to thousands of end-devices through MAC commands on the downlink. However, most of these allocation strategies are validated either by simulation or mathematical models. In this paper, we discuss the fundamentals and the requirements of the MAC commands transmission on the downlink. Our design allows IoT applications to transmit frames on the downlink hitherto reserved for MAC configuration commands, makes easier the implementation of new spreading factor assignment strategies, and provides an experimental framework for Downlink performance analysis. We validated our proposal through extensive experimental study, demonstrated the limitation of downlink over uplink due to the sensitivity of the end-device receiver, and finally we studied the impact of traffic load on the downlink performance.

The next step towards vehicular networks in smart cities would be the deployment of autonomous shuttles with multiple on-board applications. Their need to offload task towards Road Side Units (RSUs) is inevitable, especially with a certain proportion of urgent data needing to be processed in the shortest possible delay. Therefore, QoS-provisioning appears as imperative, along with the optimization of resource requesting at RSUs. To this end, we propose CAVTOMEC, a multi-RAT location-aware, context-aware task offloading solution with QoS provisioning for MEC vehicular networks. Our solution consists of three intertwined mechanisms: traffic classification, location-awareness exploiting the contents of CAM beacons and V2N-enhanced resource polling. Traffic classification identifies high, low, and indifferent task priorities, while location and resource awareness help to select the most appropriate RSU to offload tasks to depending on these priorities. Performance evaluations show that our proposal offers better load balancing at the RSUs than traditional offloading schemes, thus satisfying high priority task offloading at better rates and freeing up more resources in case an unexpected event occurs.

The main goal of this work is to set up a solution that allows the V2X system to optimize network resources selection under a certain number of constraints related to the quality of service and energy consumption. In this paper, we design a novel neighboring scheme to enhance the existent communication policies of awareness messages. Vehicles and Vulnerable Road Users (VRUs) (such as pedestrians, cyclists, motorized two wheels, etc) will be able to efficiently adapt the frequency of sending awareness messages based not only on the variation in location dynamics (e.g. velocity, position, direction) but also on the environment's context. Context-aware and dynamic adaptation of the transmission periodicity undoubtedly helps in reducing unnecessary network signalization and saving the energy of the VRUs' connected devices. To this end, we demonstrate how the proposed scheme can optimize the network load of message transmission and thus the energy consumption especially for VRUs. Further, we study the impact of the adaptation on the reliability and latency of VRU protection system. Simulation results suggest that there is an interesting gain in network and application related metrics with the best configurations of messages periodicity.

Unmanned Aerial Vehicle (UAV)-aided Wireless Sensor Network (WSN) has been received increasing attentions in recent years due to its tremendous applications in many fields. The high dynamic topology in such network brings new communication challenges. In this paper, we address on the dynamic parameters, including the velocity and flying height of the UAV, the sensors velocities, and its real-time location, which affect the topology and influence the communication performance of the system. We introduce the contact-duration to mathematically integrate these parameters and to measure the transmission opportunity between the mobile node and the UAV and formulate the data collection issue as an optimization problem with the objectives of jointly maximizing the number of collected packets and the number of nodes that participate the communications with the UAV. Furthermore, we propose an opportunity-optimal frame selection algorithm, named as OFS algorithm, to increase the communication opportunities of the nodes, thereby, the data collection performance of the network was enhanced. Through extensive simulations based on simulated movement and real experiments, we evaluate the effectiveness of the proposed mechanisms under different configurations. The results present that the proposed mechanisms perform well on enhancing the transmission opportunity of the mobile nodes and the data collection performance of the network.

LoRaWAN is a promising enabling technology for connected things. Nowadays, it is largely used in many application areas thanks to its relevant features such as long-range, low-cost, and low power consumption. However, as with all wireless technologies, mobility remains a major concern bringing end-devices out of their home operator coverage. In this paper, we investigate the inter-operator roaming capability in mobile scenarios. We proposed a novel LoRaWAN roaming scheme to enable inter-operator roaming based on DNS resolution and end-device context migration between networks. Moreover, we extended the LoRaWAN architecture while maintaining the integrity of the existing mechanisms and with a minimum prior configuration requirements. In order to validate our solution, we designed and implemented a test-bed platform integrating our extensions to Chirpstack.

Although LoRa modulation is known for its robustness allowing devices to communicate kilometers away, it suffers from coverage issues especially where density of gateways is low or in dense urban areas. However, a simple 2-hop LoRaWAN communication can seamlessly extend the network coverage and even improve both data extraction rate (DER) and energy consumption. Experiments in this paper figure out cases under non line of sight (NLoS) conditions where relaying performs better. Regarding the exponential increase of airtime with the spreading factor (SF), as soon as a 2-hop SF7 link allows a better DER as a single hop SF8 link, it becomes more attractive to use a relay. Indeed, energy consumption is linked to the airtime - or the amount of time to send a frame - explaining the energy efficiency with the 2-hop relaying protocol. To verify this assert, LoRa network coverage with a testbed in urban environmens is first compared. Then, simulations help to study energy consumption according to the case study. Results prove that relaying effectively gives better results under NLoS conditions, particularly in dense areas, by improving the DER. It also highlights the limits of LoRa in urban areas where the DER can be under 0.5 using SF12 and with less than a kilometer range.

LoRaWAN Enabled networks are expected to have a dizzying growth. Thus, an efficient allocation of wireless resources so as to support a large number of nodes is a major concern. In this paper we propose an SF assignment approach paying attention on the traffic load both per Spreading Factor and over the channels. Indeed, our strategy consists in finding a better distribution of the nodes on the SF by orchestrating an effective load balancing. Moreover, the performance of our solution is evaluated under diverse network configurations taking into account the capture effect and the non-orthogonality of SFs. In addition, we validated some assumptions by full-scale experiments like for the 3GPP path loss model which is used for the first time in LoRa simulations. Our results suggest that Load Shifting leads to better performance in terms of DER (Date Extraction Rate) while guaranteeing good scalability on the network size and density.

The Content Centric Networking (CCN) is an emerging paradigm that grounds networking primitives on content names rather than node locators. CCN targets seamless mobility, native muti-cast/multipath support, and content oriented security to better reflect the needs of today users. CCN could greatly improve the efficiency of content delivery also in Wireless Sensor Networks (WSNs). In such a context, we place our attention on the energy efficiency of caching content placement. Our objective is to save energy while achieving a high interest satisfaction rate by study the impact of certain parameters on caching strategy. To this end, we come up with a collaborative on path caching strategy that exploits the node degree and its distance from the source. Through extensive simulations, we demonstrate that our approach achieves a energy efficiency compared to a LCE (Leave Copy Everywhere) while ensuring a good cahe hit then an important interest satosfaction rate.

This paper proposes novel MAC protocols for an unmanned aerial vehicle (UAV) aided mobile wireless sensor network. UAV and sensors are mobile, and UAV collect data from sensors. In such dynamic aerial network, both the physical contact duration time (CDT) and the data-rate (DR) between mobile nodes and the UAV impact the data collection deeply. We propose hybrid beacon based MAC schemes combing CSMA/CA with physical parameters based scheduling. The UAV broadcasts ‘Beacon’ evenly to its coverage, and the mobile nodes that receive the ‘Beacon’ will randomly access to the channel through CSMA/CA. We compare fixed inter-beacon duration combined with a proactive scheduling MAC to an beacon-based IEEE 802.15.4. Through extensive simulations, the proposed MAC protocols have high performance in average delivery ratio and fairness compared to beacon beacon-based IEEE 802.15.4.

This paper studies the opportunistic routing (OR) in unmanned aerial vehicle (UAV) assisted wireless sensor networks (WSNs). We consider the scenario where a UAV collects data from randomly deployed mobile sensors that are moving with different velocities along a predefined route. Due to the dynamic topology, mobile sensors have different opportunities to communicate with the UAV. This paper proposes the All Neighbors Opportunistic Routing (ANOR) and Highest Velocity Opportunistic Routing (HVOR) protocols. In essence, ANOR forwards packets to all neighbors and HVOR forwards them to one neighbor with highest velocity. HVOR is a new OR protocol which dynamically selects route on a pre-transmission basis in multi-hop network. HVOR helps the sensor which has little opportunity to communicate with the UAV to determine which sensor, among all the sensors that are within its range, is the forwarder. The selected node forwards the packet. As a result, in each hop, the packet moves to the sensor that has higher opportunity to communicate with the UAV. In addition, we focus on various performance metrics, including Packets Delivery Ratio (PDR), Routing Overhead Ratio (ROR), Average Latency (AL) and Average Hop Count (AHC), to evaluate the proposed algorithms and compare them with a Direct Communication (DC) protocol. Through extensive simulations, we have shown that both HVOR and ANOR algorithms work better than DC. Moreover, the HVOR algorithm outperforms the other two algorithms in terms of the average overhead.

Unlike other networks, WSNs have the particular characteristic of collecting sensed data and forwarding it to the gateway through a one-way communication protocol. In such data-driven networks, it may seem spontaneous to argue to content-centric networking communication architecture. Through exploiting DFCCN-WSNs implemented previously, we focus on two approaches concerning content update in CCN for Wireless Sensor Networks. Techniques used for content update are either reactive or proactive. In addition, our approaches support multi-user scenarios in order to decrease the propagation of the content object in the network. Through extensive simulations, we demonstrate that the two approaches outperforms traditional CCN in terms of end-to-end delay and energy consumption.

Wireless Sensor Networks (WSNs) are an important tool for monitoring strategic and dangerous sites where high security is required. Failure detection for sensor nodes in this specified application is a major concern. The failure of any system may cause losses such as economical, equipment damage and even risks for human lives. Moreover, failures are unavoidable in WSNs due to hardware constraints, hostile environment, unattended deployment and limited resources. This paper proposes a fully distributed approach, called Boundary Node Failure Detection (BNFD), for an efficient boundary control based on the determination of the WSN boundary. This one is determined using an algorithm which has the property to determine in each iteration the one-hop neighbor of the current boundary sensor. Hence, each boundary sensor knows its direct next boundary neighbor and can communicate with it in order to periodically test its presence. When a situation of failure is detected, a network restructuring will be launched to find a new boundary and an alarm will be triggered. The proposed approach has been implemented and simulated with the Castalia simulator. The simulation results show that the proposed method is energy efficient.

IEEE 802.15.4 protocol is very much associated with ZigBee protocol and targets low data rate, low power consumption and low cost wireless networking that fits the requirements of wireless sensor networks (WSNs). Due to increase in radio frequency (RF) based communication devices and spectrum sharing between these devices makes it impossible to neglect the affect of interference on wireless communications. This paper provides brief description about technical features of IEEE standard 802.15.4 and ZigBee based physical layer structure and wireless channel impulsive interference modeling for wireless sensor networks in smart cities applications. For more accurate estimation of wireless channel characteristics, we have modeled it using alpha-stable distribution function. We have then estimated two out of four parameters of alpha-stable distribution depending on spatial density of wireless devices in specified area around the wireless node in a network. This allows better modeling and estimation of wireless channel conditions. We have analyzed IEEE standard 802.15.4 based communication system performance in terms of the bit error rate (BER). We have varied the total number of active nodes in specified area around sensor node and evaluates its affect on overall BER performance. We have also integrated this PHY layer based on IEEE standard 802.15.4 in the CupCarbon simulator with the consideration of impulsive network interference and its modeling using alpha-stable distribution.

In this paper, we study data collection in mobile wireless sensor networks (WSNs) assisted by unmanned aerial vehicle (UAV). We focus on randomly deployed mobile sensors along a predefined path with different but constant velocities, and a flying UAV in different heights to collect data from the mobile sensors. As the network topology is changing under the mobility of the UAV and the sensor nodes, the design of efficient data collection protocols is a major concern. In this paper, we propose four data collection algorithms taking into account the multi-data-rate transmissions (DR) and the contact duration time (CDT) between the sensors and the UAV. Besides, we propose a fairness metric to evaluate the algorithms. Through extensive simulations, we examine the effectiveness of the proposed algorithms under different configurations and show how the algorithm combining DR and CDT outperforms the others in terms of number of collected packets and weighted fairness.

Almost all WSNs are deployed with some redundancy degree which is used only for robustness objectives. If not handled in an intelligent way, redundancy results in energy wasting because of redundant transmission and reception operations. In view of this energy wasting, a redundancy detection method under sensing models is proposed. We propose to take benefit from measurement redundancy to optimize the energy consumption and improve the end-to-end delay. We propose OER (Optimization of Energy based on Redundancy) protocol, to improve energy consumption in WSNs.

Health-care monitoring with Wireless Sensor Networks (WSN) has become a major interest during the last few years. The use of efficient communication protocols is crucial in minimizing transmission delay and energy consumption of sensor nodes. In a heterogeneous WSN context, we propose, efficient mobility aware mechanisms which may be implemented in large three-tier WSN. These mechanisms must be designed to be optimal. The aim of this paper addresses common sensor behavior such as data exchange meanwhile responding to the mobility issue in the fore mentioned context. The mechanisms are based on two main protocols: topology creation and data collection in intra- and inter-tiers. Our mechanisms minimize collisions and optimize the duty-cycle of each node. Furthermore, a performance evaluation study detailed by an analytical model is presented in order to validate the designed protocols.

Efficient transmission of variable-bit rate (VBR) video traffic in Broadband Wireless Access (BWA) networks is currently an active research topic. Due to the dynamic changes in bandwidth requirements of VBR video and the limited network bandwidth, dynamic bandwidth allocation (DBA) is required. Traffic prediction is a promising approach to improve the effectiveness of DBA in BWA networks. In this paper, we propose DEEP (Density Estimation basEd Predictor), a novel prediction scheme for MPEG traffic. In DEEP, the density of probability of MPEG frames is estimated through kernel density estimation method. This density is then used in forecasting the future bit rate of I, P, and B video frames. Simulation results show that DEEP is able to predict the bit rate of MPEG traffic more accurately than the conventional Least Mean Squares (LMS) algorithm, and is less sensitive to traffic variation. We provide application guidelines of this predictor in the IEEE 802.16 standard.

The lifetime of Wireless Sensor Networks (WSN) is crucial. The goal of all WSN application scenarios is to have sensor nodes deployed, unattended, for months or years. In this work, we investigate the problem of energy consumption and lifetime maximizing in a many-to-one sensor network. In such network pattern, all sensor nodes generate and send data to a single sink via multihop transmissions. In our previous experimental studies, we noticed that, since all the sensor data has to be forwarded to a base station via multihop routing, the traffic pattern is highly nonuniform, putting a high burden on the sensor nodes close to the base station. In this paper, we analyze and propose some strategies that balance the energy consumption of these nodes and ensure maximum network lifetime by balancing the load as equally as possible.

Temperature control is a major concern in cold chain monitoring and traceability services for the largest cause of food poisoning. Considerable work has examined the use of wireless solutions in warehouses. However, the maintaining of the chain during the transportation phase remains very crucial. In this paper, we present new wireless sensor network (WSN) solution for cold chain monitoring. This solution is based on self-organizing protocols and aims at saving energy in order to increase the sensor lifetime and as a result the global network longevity within the given context. Furthermore, this solution does not need any base station or central equipment. To save energy, periodical active/sleep schedule is adopted in the design of our protocols which were dimensioned and validated by an analytical model described in this paper.

Wireless sensor networks (WSN) have received much attention during the last few years especially with regard to energy consumption and scalability. In this paper, we will focus on mechanisms which may be implemented for small WSNs. So, in the present work, we design energy-aware self-organization algorithms for WSNs in such a context. These algorithms can be used to design effective and adaptive protocols. The first protocol concerns the initialization or the setting up of the network topology under a chain form. The second one (steady-state protocol) implements the communication part or the information exchange between the different nodes of the chain. Our algorithms were dimensioned and validated by an analytical model. We also perform a detailed study of these algorithms by using TOSSIM, a simulation environment for TinyOS, the operating system for the Berkeley sensor nodes. Finally, we achieve an experimental test using Tmote Sky nodes, a popular commercial hardware platform for wireless sensor systems. The results emphasize the interest of the proposed algorithms.