Many post-quantum cryptographic schemes have been proposed in the literature, offering resistance to attacks launched by quantum computers. Due to its promising security properties, NTRU was selected as a candidate in the final round of the NIST competition on post-quantum cryptography. However, this method poses challenges for constrained devices due to its high computation and memory requirements. Motivated by the need to extend the lifetime of resource-constrained IoT devices while still being able to withstand a quantum attacker, in this chapter we propose a new NTRU-based collaborative encryption scheme for such devices. Our scheme preserves the confidentiality of sensitive information exchanged among constrained IoT devices deployed in an edge computing architecture. Our approach distributes the workload of the cryptographic operations among the edge nodes and IoT devices within the same network. This collaborative approach enables IoT devices to significantly reduce their computational cost while maintaining data confidentiality. Additionally, the proposed distribution of computing allows scalability and a better sustainability of IoT environments. Finally, we demonstrate through empirical evaluation and extensive simulation that our scheme is secure, improves energy saving and resource utilization in IoT networks, and is more efficient than the traditional centralized approach.

Trustworthiness is a critical element influencing every entity in fog computing architectures, which raises reliability and security issues for data exchanged between fog nodes and data sources. Malicious entities can introduce incorrect or manipulated information, requiring trust assessment and management. Maintaining trust throughout the fog computing architecture requires robust trust mechanisms, secure communication protocols, constant trust management and evaluation models. The integration of blockchain technology also provides a transparent and traceable environment, enabling the preservation of trust scores and promoting accountability. Indeed, we propose a new way of calculating event trustworthiness by exploiting trust factors such as Dempster-Shafer theory, temporal relevance and distance relevance. As a result, malicious entities can be discovered and trustworthy behavior can be promoted in the fog computing environment. Finally, we prove that our protocol is highly efficient, as demonstrated by extensive simulations and in-depth theoretical analysis that confirmed its security.

Zero-day attack detection solutions aim to proactively identify unknown threats targeting valuable assets within a given system. While many Intrusion Detection System (IDS) solutions leverage learning techniques to build novel attack detection systems, they often focus on enhancing accuracy for specific attack types, overlooking the potential for multiple attack scenarios. Therefore, we introduce a novel framework for detecting zero-day attacks that evade current detection systems. Our framework enhances attack identification and qualification through a hybrid learning approach, where supervised learning ensures detection of known attacks and unsupervised learning. It encompasses intrusion detection phases from data collection to new attack class detection by identifying anomalies in real-time network flow data. Unsupervised learning, which involves grouping similar data points into clusters, establishes minimum distances within these clusters. This process triggers cluster division when certain thresholds are reached. Finally, an online supervised learning process validates our approach’s effectiveness in identifying anomalies associated with zero-day attack flows. This approach significantly reduces the False Detection Rate (FDR) without solely focusing on optimizing machine learning (ML) and deep learning (DL) algorithms hyper-parameters. We evaluated our framework on two datasets: one from a real industrial context at IBM and the NSL-KDD dataset. The results demonstrate our framework’s ability to detect anomalies in previously zero-day attack targets. On average, we identified 71 anomalous flows per target, achieving an overall average online learning accuracy of 98.4% for the IBM dataset and 96.6% for the NSL-KDD dataset, thereby validating the detection of these new attack scenarios.

Industrial IoT (IIoT) networks must provide reliability, determinism and security. In terms of security, ensuring an efficient authentication is still a challenging task. Most of the authentication approaches adopted in IIoT wireless communication protocols rely on the existence of a pre-shared key (PSK) between each joining node and the central authority of the network. How to share the PSK is however not specified in their standards. In this paper, we propose a new zero-touch mutual authentication and key establishment protocol for IIoT. In our protocol, the network coordinator authenticates a new joining node using certificates. Then, the joining node authenticates the network coordinator through a novel consensus achieved among nodes that are already in the network. Our novel consensus is based on Shamir secret sharing, and allows each new node to build its trust based on the knowledge of a group of nodes that are already in the network. Finally, our protocol allows to securely establish a common key between new joining nodes and the network coordinator over a public channel. In addition to the theoretical aspects, we evaluate the performance of our protocol under two attack scenarios where 33% of the nodes in the network are malicious. We show in all cases that we efficiently ensure authentication with high success probabilities, by establishing a consensus including a limited number of nodes in the network.

Fog computing is a promising computing paradigm that provides computing services close to end-users at edge of the network. Therefore, it supports large-scale, geographically distributed and latency-sensitive applications. However, due to its un-trusty nature, numerous security challenges must be overcome. The integrity verification of the data in fog computing context is one of the main challenges to tackle. Indeed, most of existing solutions consider a centralized data storage context, and usually rely on a trusted third party to check data integrity. However, these approaches are not suitable for fog computing architecture, since the data is stored in a dynamic and a completely distributed manner. Motivated by these challenges, we propose in this paper, a new efficient public verification scheme that protects the integrity of the data in fog computing architecture. Our scheme secures data integrity and authenticity based on the short integer solution SIS problem and the identity-based signatures. In addition, it allows to efficiently verify the integrity of data, even when it is separately shared on multiple servers. This verification can be performed by any end-user on the architecture, and without relying on any trusted third party. Finally, we show through extensive simulation that our solution is highly effective and outperforms existing solutions.

The Internet of Things (IoT) represents an eclectic paradigm that is still growing in popularity. However, security aspects are a major concern for IoT devices due to their applications and the amount of sensitive data they provide. Simultaneously, the energy constraint in IoT networks remains a significant issue due to their limited resources. To reduce their energy consumption, several IoT protocols have integrated the energy-saving mode which offers four operating modes. On the basis of these four states, we derive an analysis framework, named HARPAGON, allowing an attacker to maximize his attack efficiency and minimize his impact in terms of energy consumption. Indeed, the effectiveness of many attacks depends principally on the state of the attacker and the victim at the same time. HARPAGON with the help of Markov chain theory allows modeling the interaction between the attacker and its victims. In this article, we demonstrate the effectiveness of the framework coupled with a jamming attack by comparing it to other types of jamming attacks. Experimental results reveal HARPAGON combined with a jamming attack drastically reduces the performance of the network, with an impact on the packet error rate (PER), which is around 13% higher than the reactive attack and with a reduced energy budget in respect of the other two well-known “green” jamming attacks.

The IPv6 routing protocol for low-power and lossy networks (RPL) was standardized by the IETF to allow multiple resource-constrained devices to interconnect in an IPv6 multihop mesh network. One of its design objectives is the ability to customize the routing objectives according to different application requirements. This is achieved through the support of various routing metrics and constraints that are then used by objective functions to pick the best path. Another objective is the ability to scale to networks composed of hundreds to thousands of devices.In this paper, we focus on the specific topic of multi-instance, a feature discussed in the RPL standard but not investigated through much research, a situation we attribute to a lack of publicly available implementation. We detail the architecture of RPL when operating in a multi-instance environment, clarify parts of the standard related to forwarding packets along a specific instance and present an implementation based on ContikiRPL we developed during the last few years. We detail some of our design choices. Then, using our prototype, we present a first systematic performance evaluation of multi-instance RPL through experiments in a large scale testbed. Our results indeed show a tipping-point when the number of instances becomes excessive but more importantly confirm that a limited number of instances can efficiently cohabit on a common network without negatively impacting the packet delivery ratio and RPL convergence time.

Companies like Netflix increasingly use the cloud to deploy their business processes. Those processes often involve partnerships with other companies, and can be modeled as workflows. This shift towards the cloud environment has led to more and more data leaks and breaches, resulting in huge losses of money for businesses like the movie industry, as well as a loss of user privacy for businesses dealing with user data like the pharmaceutical industry.In this paper, we show how those workflows can be enforced while preventing data exposure. Following the principles of zero-trust, we develop an infrastructure using the isolation provided by a microservice architecture, to enforce owner policy. We show that our infrastructure is resilient to the set of attacks considered in our security model. We implement a simple, yet realistic, workflow with our infrastructure in a publicly available proof of concept. We then verify that the specified policy is correctly enforced by testing the deployment for policy violations, and estimate the overhead cost of authorization.

Companies like Netflix increasingly use the cloud to deploy their business processes. Those processes often involve partnerships with other companies, and can be modeled as workflows where the owner of the data at risk interacts with contractors to realize a sequence of tasks on the data to be secured.In practice, access control is an essential building block to deploy these secured workflows. This component is generally managed by administrators using high-level policies meant to represent the requirements and restrictions put on the workflow. Handling access control with a high-level scheme comes with the benefit of separating the problem of specification, i.e. defining the desired behavior of the system, from the problem of implementation, i.e. enforcing this desired behavior. However, translating such high-level policies into a deployed implementation can be error-prone.Even though semi-automatic and automatic tools have been proposed to assist this translation, policy verification remains highly challenging in practice. In this paper, our aim is to define and propose structures assisting the checking and correction of potential errors introduced on the ground due to a faulty translation or corrupted deployments. In particular, we investigate structures with formal foundations able to naturally model policies. Metagraphs, a generalized graph theoretic structure, fulfill those requirements: their usage enables to compare high-level policies to their implementation. In practice, we consider Rego, a language used by companies like Netflix and Plex for their release process, as a valuable representative of most common policy languages. We propose a suite of tools transforming and checking policies as metagraphs, and use them in a global framework to show how policy verification can be achieved with such structures. Finally, we evaluate the performance of our verification method.

Smart Cities rely on a broad variety of services generating an heterogeneous traffic load, and having diverse privacy and performance requirements. In this paper, we focus on mobility services for an Intelligent Transportation System (ITS), where several applications inter-operate by sharing information. In particular, we consider three main services: ride-hailing, smart parkings and traffic regulation. To investigate the load generated by these mobility services, we rely on two real datasets. Our goal is to study whether a given technology can scale with realistic conditions related to the envisioned smart mobility scenario. In particular, we analyze here how many LoRa gateways are necessary and how they should be deployed to support these mobility services. We highlight the bottlenecks occurring in most dense areas of the network and argue that heterogeneous deployments can efficiently handle such hot spots.

Wireless networks are prone to jamming-type attacks due to their shared medium. An attacker node can send a radio frequency signal and if this signal interferes with the “normal” signals of two communicating nodes, the communication can be severely impacted. In this paper, we examine radio interference attacks from the jamming node perspective. In particular, we assume a “greedy” jamming node, whose main twofold objectives are to attack and interfere the communication of a transmitter and a receiver node, by minimizing its energy consumption and maximizing the detection time. The two communication nodes are static during the attack window time, while the attacker node can adapt its distance from the transmitter in order to select the most suitable range for a successful interference. In order to take into account the distance factor for the effectiveness of the attack, we derive an optimization model for representing the attack and we will study the key factors that allow effective and efficient implementation of a jamming attack, namely a) the energy b) the detection time and c) the impact on the transmission in terms of lowering the PDR. Three different types of attacks will be analyzed, 1) Constant Jamming, 2) Random Jamming and 3) Reactive Jamming. Simulation results show that the effectiveness of a jamming attack in respect to the others not only depends on the position of the jamming node but also on the distance between the transmitter and receiver nodes.

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.

Industrial networks differ from others kinds of networks because they require real-time performance in order to meet strict requirements. With the rise of low-power wireless standards, the industrial applications have started to use wireless communications in order to reduce deployment and management costs. IEEE802.15.4-TSCH represents currently a promising standard relying on a strict schedule of the transmissions to provide strong guarantees. However, the radio environment still exhibits time-variable characteristics. Thus, the network has to provision sufficient resource (bandwidth) to cope with the worst case while still achieving high energy efficiency. The 6TiSCH IETF working group defines a stack to tune dynamically the TSCH schedule. In this paper, we analyze in depth the stability and the convergence of a 6TiSCH network in an indoor testbed. We identify the main causes of instabilities, and we propose solutions to address each of them. We show that our solutions improve significantly the stability.

Smart Cities need real time information to improve the efficiency of their transportation systems. In particular, crowd sensing may help to identify the current speed in each street, the congested areas, etc. In this context, map matching techniques are required to map a sequence of GPS waypoints into a set of streets on a common map. Unfortunately, most map matching approaches are probabilistic. We propose rather an unambiguous algorithm, able to identify all the possible paths that match a given sequence of waypoints. We need an unambiguous identification for each waypoints set. For instance, the actual speed should be assigned to the correct set of streets, without error. To identify all the possible streets, we construct the set of candidates iteratively. We identify all the edge candidates around each waypoint, and reconstruct all the possible sub-routes that connect them. We then verify a set of constraints, to eliminate impossible routes. The road segments common to all computed routes form an unambiguous match. We evaluate the matching ratio of our technique on real city maps (London, Paris and Luxembourg). We also validate our approach with a real GPS trace in Seattle.

Recent boom of mobile applications has become an essential class of mobile Internet of Things (IoT), whereby large amounts of sensed data are collected and shared by mobile sensing devices for observing phenomena such as traffic or the environmental. Currently, most of the proposed Medium Access Control (MAC) protocols mainly focus on static networks. However, mobile sensor nodes may pose many communication challenges during the design and development of a MAC protocol. These difficulties first require an efficient connection establishment between a mobile and static node, and then an efficient data packet transmissions. In this study, we propose MobIQ, an advanced mobility-handling MAC scheme for low-power MAC protocols, which achieves for efficient neighbour(hood) discovery and low-delay communication. Our thorough performance evaluation, conducted on top of Contiki OS, shows that MobIQ outperforms state-of-the-art solutions such as MoX-MAC, MOBINET and ME-ContikiMAC, in terms of significantly reducing delay, contention to the medium and energy consumption.

This demonstration aims at observing in an interactive manner the impact of modification of preamble and sampling periods at the low-power family of MAC protocols, and thus, illustrating in real-time the energy consumption and delay performance of each node accordingly. To do so, we implemented the ability for users to generate traffic at some remote nodes that are involved in two distinct deployed topologies. Those deployed networks operate with either a statically configured network, by employing X-MAC on top of the Contiki OS, or T-AAD, a lightweight traffic auto-adaptive protocol that allows live and automatic modifications of duty-cycle configurations.

In traditional routing protocols designed for Wireless Sensor Networks, each sensor node is related to one or more neighbors that will forward its readings up to the sink. This technique performs well for static topologies with homogeneous configurations, but usually fails to cope with network dynamics such as mobility and node failures. Opportunistic routing is an approach to address this particular problem. In this context, the data packets are addressed to a set of potential forwarders and then forwarded by the neighbor that first acknowledges the message. Yet, several former studies demonstrated that in some cases, a single packet may be forwarded by multiple neighbors simultaneously. This situation leads to packet duplication and consequently to increased channel occupancy and energy consumption in the network. In this paper, we study to what extent the previously reported phenomenon depends on both the topology density and the nodes MAC configuration. We then introduce a mechanism that handles the potential deafness in the network through heterogeneous configuration among the nodes in the network. We do so through local, dynamic and automatic MAC parameters adaptation, in order to reduce unnecessary traffic, channel occupancy and energy consumption due to packet duplication in opportunistic networks. Finally, we provide both theoretical analysis and experimental campaign to detail the benefits of our approach.

Due to their physical constraints and deployment conditions, power management is a key issue in Wireless Sensor Networks. The radio component being the most responsible for energy wastage, controlling medium access is of prime importance in order to obtain desired network lifetime. Several existing mechanisms realize energy gains by alternating active and passive periods at the radio scale and most real WSN deployments implement static and homogeneous duty-cycling (i.e. invariant and identical for each node in the network). Although preventing any node isolation, such method fails to address the dynamics of the network efficiently. We propose a strategy to enable heterogeneous MAC duty-cycle configurations among nodes in the network. We aim at granting each node a specific sleep-depth, according to criteria specific to the deployment (e.g. applicative criteria, location in the routing structure). To implement this idea, sensor nodes are divided into disjoint subsets, each of them standing for a given duty-cycle configuration and leading to a network performance managed at its best (e.g. energy consumption, loss rate). We detail to what extent our approach preserves network connectivity with coherent heterogeneous duty-cycling, thus reaching a compromise between energy consumption and reactivity. The presented experimental campaign was led over the SensLAB testbed. It demonstrates that our solutions provide up to 61% energy saving, yet preserving the loss-rate below 10%, and guaranteeing the connectivity of the network.

Validation of protocols and mechanisms is an essential step to the development of object networks in critical domains. Most papers still provide evaluation either obtained through theoretical analysis or simulations campaigns. Yet, simulators and formal models fail to precisely reproduce the unique specificities of the deployment environments those networks have to evolve in. Also, by putting no limits to code complexity and execution, those tools prevent users to apprehend the actual limits of WSN nodes and to propose realistic communication protocols and applications. In this paper, we highlight to what extent the addition of experimentations can significantly improve the value of performance evaluation campaigns. Along with the recent tendency to have algorithmic and protocol proposals facing real environments, it is questionable whether the so obtained results should be considered as scientific or empirical ones. In the former case, reproducibility, stability over time, topology management to cite a few, are a must have for testbeds and real deployments that are used. In the latter, the results should be viewed as a proof-of-concept only, far from independent of the used hardware and encountered conditions at the experimentation time but still critical from the development cycle standpoint. Through some experiments over the IoT-LAB testbed, we aim at demonstrating to what extent some of the simulation setup and conditions from reality could be emulated. We also provide insight on how to obtain the best out of it in a quick and efficient manner. We show that such testbeds would satisfy many expectations (e.g. scientific tool and proof-of-concept validator), thus minding and bridging some of the gaps between theory and practice in WSN. To this end, we here give an overview of available simulation tools, and guidelines on how to transpose simulation setups to the open large-scale IoT-LAB platform.

In wireless sensor networks, energy efficiency is generally achieved by turning off some capabilities from a subset of deployed sensors. The set of active nodes must therefore meet the application requirements (e.g. area coverage, data redundancy) while remaining fully connected to allow further data collection. Here, we focus on the case of a nomad sink entering the network and gathering every monitoring data. At the routing layer, minimizing the number of nodes acting as relays requires to construct a maximum leaf spanning tree (MLST). However, optimizing convergecast communications consists in minimizing the hop distance between the sink and all others nodes, leading so to a shortest path tree rooted at the sink. In this paper, we propose a distributed routing protocol that aims at constructing an energy efficient backbone being convergecast efficient at the same time. Our proposal introduces a tradeoff parameter to adjust the compromise “number of relays / routing efficiency” and then constructs a hybrid routing structure based on the combination of variants of the Wu-Li algorithm and a gradient-based routing protocol. For all topologies we simulated, and when tuned for energy saving, our approach outperforms a 2-approximation for constructing a MLST. Furthermore, when tuned for convergecast routing, simulation results show that our solution constructs a routing optimal backbone that involves a small fraction of relays.

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.

Wireless sensor networks (WSN) require pertinent and reliable data collection schemes in order to provide information about their deployment area. This article aims at detailing our contribution for message loss avoidance along prone-to-failure paths that monitoring reports would follow from sensors to sink stations. While many researchers have tackled this issue by focusing on particular layers of the embedded communication stack (e.g. medium access control or routing protocol), we propose an original layer-independent scheme. The key aspect of our solution is to create fake data sources that have acted as storage nodes during the failure of links leading to the sink station. Few control messages are required during our gradual recovery phase, thus maintaining a negligible overhead in terms of message complexity and energy consumption, and also resulting in much improved data delivery ratio.

The medium access control being the main source of energy wastage in wireless sensor networks, energy-efficiency is always kept in mind while designing the communication stack. Especially, versatile preamble-sampling MAC protocols emerged to offer a suitable solution over multiple deployment characteristics. For instance, some works have targeted burst transmissions while others have focused on long-term energy-efficiency for mostly sleeping wireless sensors. In this paper, we observe to what extent versatility applies to dynamic scenarios in which communications do not respect specific paradigms. We show that preamble-sampling protocols lack an optimal configuration for antagonist traffic patterns. We therefore introduce an auto-adaptive algorithm that can adjust its sampling period and preamble length. Our solution is based on a link-by-link negotiation in order to establish energy-efficient communications over certain paths toward the sink station. This mechanism, auto-adapting to the current network traffic, can be used in combination with various preamble-sampling MAC layers, and works for every traffic pattern, being especially energy-efficient during burst transmissions. We particularly studied its association with X-MAC, and observed that energy savings are not accomplished to the cost of reliability.

In this paper, we briefly describe an interactive roulette game enabled over a wireless sensor network platform. It basically consists in a train speeding in one way (the spinning roulette) and in a message hopping along some deployed sensors in the other way (the wheeling ball). This demonstration aims at illustrating in an interactive manner the high radio channel randomness in dense wireless sensor networks, as well as its impact on the communication protocols.

Energy efficiency is one of the most important issue to be tackled in wireless sensor networks. Activity scheduling protocols aim at prolonging the network lifetime by reducing the proportion of nodes that participate in the application. Among the vast range of criteria existing to schedule nodes activities, area coverage by connected sets is one of the most studied. Active nodes must ensure area coverage while remaining connected in order to guarantee proper data collection to the sink stations. As wireless communications stand for the main source of energy consumption, we investigated the communication redundancy of the active nodes set. We define a path diversity based metric that allows to characterize the communication redundancy of a given set of nodes. We show that one of the most used connectivity criterion is far from building minimal connected sets in terms of communicating nodes involved. Our results open new directions to design localized connected sets solutions.

We propose several localized sensor area coverage protocols for heterogeneous sensors, each with arbitrary sensing and transmission radii. Sensors are assumed to be time synchronized, and active sensors are determined at the beginning of each round. The approach has a very small communication overhead since prior knowledge about neighbor existence is not required. Each node selects a random timeout and listens to messages sent by other nodes before the timeout expires. Sensor nodes whose sensing area is not fully covered (or fully covered but with a disconnected set of active sensors) when the deadline expires decide to remain active for the considered round, and transmit an activity message announcing it. There are four variants in our approach, depending on whether or not withdrawal and retreat messages are transmitted. Covered nodes decide to sleep, with or without transmitting withdrawal message to inform neighbors about the status. After hearing from more neighbors, active sensors may observe that they became covered, and may decide to alter their original decision and transmit a retreat message. Our simulation shows reduced message overhead while preserving coverage quality compared to existing method based on hello messages followed by retreat ones.

Wireless sensor networks are made up of hundreds of devices deployed over a distant or sensitive field to be monitored. Energy consumption is balanced by taking advantage of the redundancy induced by the random deployment of nodes. Some nodes are active while others are in sleep mode, thus using less energy. Such a dynamic topology should not impact the monitoring activity. Area coverage protocols aim at turning off redundant sensor nodes while ensuring full coverage of the area by the remaining active nodes. Providing k-area coverage therefore means that every physical point of the monitored field is sensed by at least fc sensor devices. Connectivity of the active nodes subset must also be provided so that monitoring reports can reach the sink stations. Existing solutions hardly address these two issues as a unified one. In this paper, we propose a localized algorithm for multiple sensor area coverage able to build connected active nodes sets. We also show that a simple feature of the protocol, called the coverage evaluation scheme, can be enhanced to handle various k-area coverage problem definitions. Experimental results show that our coverage scheme is resistant to collisions of messages as k-area-coverage of the deployment area and connectivity of the active nodes set can still be ensured.

We consider the problem of activity scheduling and area coverage in sensor networks, and especially focus on problems that arise when using a more realistic physical layer. Indeed, most of the previous work in this area has been studied within an ideal environment, where messages are always correctly received. In this paper, we argue that protocols developed with such an assumption can hardly provide satisfying results in a more realistic world. To show this, we replace the classic unit disk graph model by the lognormal shadowing one. The results show that either the resulting area coverage is not sufficient or the percentage of active nodes is very high. We thus present an original method, where a node decides to turn off when there exists in its vicinity a sufficiently reliable covering set of neighbors. We show that our solution is very efficient as it preserves area coverage while minimizing the quantity of active nodes.

We propose several localized sensor area coverage protocols, for arbitrary ratio of sensing and transmission radii. Sensors are assumed to be time synchronized, and active sensors are determined at the beginning of each round. The approach has a very small communication overhead since prior knowledge about neighbor existence is not required. Each node selects a random timeout and listens to messages sent by other nodes before the timeout expires. Sensor nodes whose sensing area is not fully covered (or fully covered but with a disconnected set of active sensors) when the deadline expires decide to remain active for the considered round, and transmit a message announcing their activity status. There are four variants in our approach, depending on whether or not negative and retreat messages are transmitted. Experimental results with ideal MAC layer show that, for a similar number of selected active sensors, our methods significantly reduce number of messages to decide activity compared to existing localized protocol, we also consider a MAC layer with collisions, and show that existing compared method, for dense networks, fails to cover the area reasonably. Our methods, however, still remain robust in terms of high area coverage with reasonable amount of active nodes, despite some message collisions.

Sensor networks consist of autonomous nodes with limited battery and of base stations with theoretical infinite energy. Nodes can be sleep to extend the lifespan of the network without compromising neither area coverage nor network connectivity. This paper addresses the area coverage problem with equal sensing and communicating radii. The goal is to minimize the number of active sensors involved in coverage task, while computing a connected set able to report to monitoring stations. Our solution is fully localized, and each sensor is able to make decision on whether to sleep or to be active based on two messages sent by each sensor. The first message is a "hello" message to gather position of all neighboring nodes. Then each node computes its own relay area dominating set, by taking the furthest neighbor as the first node, and then adding neighbors farthest to the isobarycenter of already selected neighbors, until the area covered by neighbors is fully covered. The second message broadcasts this relay set to neighbors. Each node decides to be active if it has highest priority among its neighbors or is a relay node for its neighbor with the highest priority.