International audience

In resource-constrained Internet of Things (IoT) environments like Low-power and Lossy Networks (LLNs), efficient communication protocols are essential. In this context, IP multicast protocols play a crucial role, facilitating the transmission of data packets from a single source to multiple recipients, thereby conserving bandwidth, power, and time for numerous LLN applications, such as over-the-air programming, information dissemination, and device configuration. Despite their usefulness, existing multicast solutions face several challenges, including scalability, energy efficiency, and reliability. To tackle such issues, this paper introduces Protocol Independent Multicast for LLNs (PIM-LLN). PIM-LLN employs a multicast distribution tree anchored at the border router, a multi-path data dissemination mechanism, and an efficient retransmission technique to route streams exclusively to regions with group members reducing energy consumption and bandwidth usage while improving response times and reliability. Through comprehensive simulations and public testbed experiments, we meticulously assess PIM-LLN’s performance, benchmarking it against state-of-the-art solutions under different scenarios. Our findings underscore the scalability, reliability, reduced latency, and efficient resource utilization of PIM-LLN in terms of memory, bandwidth, and energy. Notably, PIM-LLN, as compared to state-of-the-art solutions, achieves a similar level of reliability while reducing overhead by up to 50%.

Belief Functions Theory (BFT), also known as Dempster-Shafer theory, has emerged as a powerful framework for uncertain modeling and reasoning in various domains. At the heart of BFT, lies the concept of combination rules, which govern the fusion process and have a profound impact on the quality and reliability of the results. Currently, there is a noticeable trend in the field, with a growing emphasis on cardinality-aware combination rules, reflecting the need for more nuanced and context-aware approaches to uncertainty management. Despite this, there exists a notable gap in the implementation of cardinality-aware rules within existing frameworks, limiting their applicability in complex decision-making scenarios. This paper addresses this gap by proposing an enhanced MATLAB framework that optimizes computational complexity, thereby enabling efficient implementation of both current and future cardinality-aware combination rules. By providing detailed insights into the framework’s structure, key functions, and examples of the implementation of these cardinality-aware rules, this paper aims to bridge the identified gap and enhance the usability of BFT in practical applications. The source code for this enhanced framework has been shared with the community.

Due to the limitations associated with the use of a single type of data during the recommendation process, recent research has focused on developing new fusion-based recommenders that make use of multiple heterogeneous sources of information to provide more accurate suggestions. However, the realistic and flexible methods available to users for expressing their preferences for products and services inherently generate uncertain, imperfect, and ambiguous data that feed recommenders and thus affect their accuracy. As a result, Recommender Systems (RS) make significant use of soft mathematical tools to deal with uncertainty. Among these tools is Dempster-Shafer Theory (DST), which has been shown to be effective at dealing with the inherent uncertainty in numerous applications. This article provides a survey of the use of DST in the RS field. Thus, after a brief introduction to recommender systems and the DST, this survey discusses recent DST applications in RS. It introduces a new taxonomy that encompasses the primary application context for DST-based RS solutions, as well as a comprehensive multi-criteria analysis of the peer-reviewed papers. The resulting comparisons are analyzed to draw conclusions, identify current study limitations, and define future research directions. This survey serves as a valuable resource for the entire research community that is interested in recommender systems and DST.

Deploying wireless sensor networks (WSNs) in real-world applications is still a challenging problem. Indeed, the number, locations, and orientations of directional sensors determine the topology of the WSN, which will further influence its overall cost and performance. Existing works have assumed simple sensor behavior and/or ideal environmental factors; thus, often they do not produce practical results. On the contrary, in this paper, we revisit the 3D WSNs deployment problem while considering realistic assumptions regarding the modeling of both the sensors and the environment. More precisely, we propose a Bresenham line-of-sight based realistic coverage model for 3D environments. This latter is used to re-formalize the 3D WSNs deployment problem while considering a realistic spatial model of the environment. The problem is then solved using a multi-objective genetic algorithm endowed with new adaptive and guided genetic operators. Moreover, we enhance the performance of the proposed approach by introducing two optimization techniques, namely: search space reduction and sampling-based evaluation. We show the effectiveness and efficiency of the proposed approach through extensive simulations.

Arbitrary point-to-point (P2P) routing is becoming a necessity in a multitude of Internet-of-Things (IoT) applications, including building automation, smart cities, and smart manufacturing. For this reason, new P2P routing protocols such as lightweight on-demand ad hoc distance-vector routing protocol next generation (LOADng) and ad hoc on-demand distance vector routing-based RPL protocol (AODV-RPL) are being standardized. Both protocols are inspired by AODV, and hence, they might suffer from broadcast storms caused by flooding route requests (RREQs) and related issues. Thus, while AODV-RPL takes advantage of RPL mechanisms, LOADng uses blind flooding to forward RREQs. However, both lack effective techniques for avoiding unnecessary RREQ transmissions when routes are found. In this article, we first deploy stopping Trickle timers to ensure scalable, efficient, and reliable dissemination of RREQs. Second, we devise and present new techniques to suppress unnecessary RREQs with optimizations for radio duty-cycled networks. Finally, the two mechanisms are combined in a third proposal for better efficiency. These mechanisms provide individually and collectively great enhancements to P2P routing protocols, such as AODV-RPL and LOADng, while staying backward compatible with their base specifications. The performance of the proposed mechanisms when applied to LOADng has been validated using both extensive time-accurate simulations and large-scale public testbeds. The obtained results have shown the effectiveness of the proposed mechanisms with around 50% less overhead and savings in energy consumption, along with a 20% gain in route discovery ratio at the expense of an increase in discovery delays.

To extend the lifetime of wireless sensor networks, recent works suggest the use of relay nodes.This paper surveys and examines representative approaches dealing with relay nodes deployment.It also discusses their shortcomings and presents a comparative study.Additionally, this paper provides a set of remarks and recommendations to improve the usage of relay nodes in wireless sensor networks and highlights open issues that need further investigation.

The coverage of a Region of Interest (RoI), that must be satisfied when deploying a Wireless Sensor Network (WSN), depends on several factors related not only to the sensor nodes (SNs) capabilities but also to the RoI topography.This latter has been omitted by most previous deployment approaches, which assume that the RoI is 2D.However, some recent WSNs deployment approaches dropped this unrealistic assumption.This paper surveys the different models adopted by the state-of-theart deployment approaches.The weaknesses that need to be addressed are identified and some proposals expected to enhance the practicality of these models are discussed.

We propose to augment the Trickle algorithm with contextual information freely and locally available in low-power wireless networking technologies. The aim is to equip Trickle with hints and heuristics allowing it to propagate updates faster using link indicators such as received signal strength and link quality indicators along with available neighbourhood and network state information. The proposed augmentations are carefully designed so to preserve Trickle strengths in terms of simplicity, reliability, scalability, and load balancing while minimising its latency. Extensive simulation evaluations, conducted under TinyOS, show that the resulting algorithm, dubbed Trickle++, propagates updates more than twice faster than Trickle. Obtained results also show that Trickle++ preserves Trickle performance regarding overhead, load balancing, and code footprint.

Content-Centric Networking (CCN) is a promising implementation of the Information-Centric Networking architecture that was introduced to address recent needs related to the proliferation of multimedia traffic in the Internet. One of the fundamental features of CCN is in-network caching that aims to reduce delays, optimize bandwidth, and resource usage. Designing better in-network caching strategies along with optimal replacement policies is a challenging research issue in CCN. Moreover, the lack of a common comparison framework has obfuscated the real performance of these strategies when compared with each other. Based on realistic assumptions and a common environment, this paper gives a comprehensive view on performance comparison of popular caching strategies and replacement policies employed in CCN, while considering different forwarding strategies and multiple network topologies.

This paper proposes a new QoS-aware forwarding strategy for Named Data Networking. Borrowing techniques from the ant colony optimization, the proposed strategy, which is called Ant colony based QoS-aware forwarding strategy (AC-QoS-FS), makes full use of both forward and backward ants to rank interfaces. Forward and backward ants (Interest and Data packets) probe realtime network QoS parameters to update the interfaces ranking in order to select the best one for forwarding the incoming Interests. The effectiveness of AC-QoS-FS is validated through ndnSIM simulation.

The first step in the deployment of wireless sensor networks for real-life applications is sensor placement. This step is of primary importance as it not only determines the network's cost, but also affects energy consumption, network lifetime, and the quality of service provided by the network. In this paper, we tackle the sensor placement problem that is NP-hard. The goal is to optimize the number of sensors and their locations in order to fulfill end-user needs while reducing the cost of the network. To this end, we propose four heuristics that wisely exploit the structure of the problem to deliver near-optimal solutions in a reasonable time. Simulation results show that our heuristics outperform previous solutions.

When several networks (e.g., Wi-Fi, UMTS, and LTE) cover the same region, the mobile terminals that are equipped with multiple network interfaces provide the possibility for mobile end-users to select their believed best network. This is known as the network selection problem, which is a decision making problem with multiple criteria (network conditions, service requirements, terminal characteristics, and user needs). Many network selection solutions using different mathematical theories have been proposed in the literature to allow the best connectivity for applications, users, and terminals. Unfortunately, most approaches for the network selection do not make effective selection decisions, since they are vulnerable to the uncertainty and imprecision related to network state information. In this paper, we investigate the belief functions theory to devise an efficient lightweight uncertainty- aware network interface selection scheme. We provide analytical studies and simulation experiments to demonstrate the efficiency of the proposed solution.

Information-Centric Networking (ICN) is a novel paradigm for future Internet architectures. The aim of ICN is to accommodate content distribution within the Internet infrastructure. Named Data Networking (NDN) is one of the most popular ICN proposal. This paper presents a design of QoS-FS, a new NDN's adaptive forwarding strategy with quality of service (QoS). At each node of the network, QoS-FS monitors, in real-time, ingoing and outgoing networks' link to estimate the QoS parameters and integrate them into the different decisions taken to determine when and which interface to use to forward an Interest. Therefore, making forwarding decision adaptive to network conditions and user's preferences. We provide simulation experiments to demonstrate the efficiency of the proposed solution.

One of the fundamental services provided by a wireless sensor network (WSN) is the monitoring of a specified region of interest (RoI). Considering the fact that emergence of holes in the RoI is unavoidable due to the inner nature of WSNs, random deployment, environmental factors, and external attacks, assuring that the RoI is completely and continuously covered is very important. This paper seeks to address the problem of hole detection and healing in mobile WSNs. We discuss the main drawbacks of existing solutions and we identify four key elements that are critical for ensuring effective coverage in mobile WSNs: 1) determining the boundary of the RoI, 2) detecting coverage holes and estimating their characteristics, 3) determining the best target locations to relocate mobile nodes to repair holes, and 4) dispatching mobile nodes to the target locations while minimizing the moving and messaging cost. We propose a lightweight and comprehensive solution, called holes detection and healing (HEAL), that addresses all of the aforementioned aspects. The computation complexity of HEAL is O(v2) , where v is the average number of 1-hop neighbors. HEAL is a distributed and localized algorithm that operates in two distinct phases. The first identifies the boundary nodes and discovers holes using a lightweight localized protocol over the Gabriel graph of the network. The second treats the hole healing, with novel concept, hole healing area. We propose a distributed virtual forces-based local healing approach where only the nodes located at an appropriate distance from the hole will be involved in the healing process. Through extensive simulations we show that HEAL deals with holes of various forms and sizes, and provides a cost-effective and an accurate solution for hole detection and healing.

A Wireless Sensor Network (WSN) consists of many sensors that are densely deployed to monitor a field. The sensor-positions can be predetermined to guarantee the quality of surveillance provided by the WSN. In remote or hostile sensor field, randomized sensor placement often becomes the only option. In this paper, we survey existing stochastic node placement strategies. We categorize stochastic placement strategies into simple and compound. A simulation study has been carried out yielding a detailed analysis of random deployment intrinsic properties, such as coverage, connectivity, fault-tolerance and network lifespan. The obtained results can give helpful design guidelines in using stochastic deployment strategies, and allow engineers to choose the deployment strategy appropriate to the situation and the goals.

Deployment is a fundamental issue in wireless sensor networks. Usually the sensor locations are precomputed based on a ”perfect” sensor coverage model, whereas sensors may not always provide reliable information, either due to operational tolerance levels or environmental factors. Therefore, it is imperative to have practical considerations at the design stage to anticipate this sensing behavior. In this paper, we address four different forms of static wireless sensor networks deployment while considering an evidence-based sensor coverage model. The four problems are formalized as combinatorial optimization problems, which are NP-complete. We propose, E2BDA (Efficient Evidence-Based sensor Deployment Algorithm), a polynomial-time uncertainty-aware deployment algorithm based on a dynamic programming approach. E2BDA is able to determine the minimum number of sensors and their locations to achieve both coverage and connectivity. We compare our proposal to the state-of-the-art deployment strategies, the obtained results show that E2BDA obtains the best performances.

In this paper, we address the issue of handling uncertainty and information fusion for an efficient WSN deployment. We present a flexible framework for collaborative target detection within the transferable belief model. Using the developed framework, we propose an uncertainty-aware deployment algorithm that is able to determine the minimum number of sensors and their locations such that full area coverage is achieved. The issues of connectivity, obstacles, preferential coverage, challenging environments and sensor reliability are also discussed. Experimental results are provided to demonstrate the ability of our approach to achieve an efficient sensor deployment by exploiting a collaborative target detection scheme.

Sensor network lifetime strongly depends on the used routing protocol. Using several definitions of lifetime and a common evaluation framework, we have analyzed the lifetime of some representative flat and hierarchical sensor network routing protocols, namely: DIRECT, FLOODING, GOSSIPING, LEACH and HEED. Extensive simulations have been carried out yielding a detailed analysis of lifetimes. Through this study, we propose a new technique to increase HEEDs' lifetime. The resulting protocol EHEED is compared to other protocols. Experimental results show that EHEED can be very effective for long-lived sensor network.

Abstract Reactive routing protocols for mobile ad hoc networks reduce the routing cost in high mobility environments where link failures are frequent. However, route discovery in these protocols is typically performedvianetwork‐wide flooding, which consumes a substantial amount of bandwidth and causes a significant latency to data packets. To improve the dynamic source routing (DSR) protocol and overcome these limitations, we propose two optimization techniques,viz. generalized salvaging mechanism and cache maintenance using a distributed topology discovery mechanism through mobile ant‐agents. We show, by simulations, that our contributions improve the DSR performance, and particularly in large scale networks with high mobility and heavy load that cause frequent link failures. Copyright © 2008 John Wiley & Sons, Ltd.