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The connected vehicle is becoming a reality, and both centralised applications provided through telecommunication infrastructure, and cooperative applications achieved directly by the vehicles through vehicle-to-vehicle communications are gaining a lot of interest from the public. Though several solutions have been investigated to ensure identification and privacy protection of vehicles participating in these applications, only a few works address the recording of exchanged messages from an audit perspective, especially in the context of vehicle-to-vehicle communications only. This paper, proposes a review of the literature on vehicular communications, and explores particularly the solutions envisaged for audit in this context, such as the Blockchain technology. We also point out the most challenging issues that should be addressed in order to achieve an effective deployment of this latter in vehicular communications, especially in the vehicle-to-vehicle communication context, which should help in developing an effective Blockchain-based audit strategy.

When a natural or human disaster occurs, time is critical and often of vital importance. Data from the incident area containing the information to guide search and rescue (SAR) operations and improve intervention effectiveness should be collected as quickly as possible and with the highest accuracy possible. Nowadays, rescuers are assisted by different robots able to fly, climb or crawl, and with different sensors and wireless communication means. However, the heterogeneity of devices and data together with the strong low-delay requirements cause these technologies not yet to be used at their highest potential. Cloud and Edge technologies have shown the capability to offer support to the Internet of Things (IoT), complementing it with additional resources and functionalities. Nonetheless, building a continuum from the IoT to the edge and to the cloud is still an open challenge. SAR operations would benefit strongly from such a continuum. Distributed applications and advanced resource orchestration solutions over the continuum in combination with proper software stacks reaching out to the edge of the network may enhance the response time and effective intervention for SAR operation. The challenges for SAR operations, the technologies, and solutions for the cloud-to-edge-to-IoT continuum will be discussed in this paper.

Four new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in wireless sensor and robot networks based on the greedy-face-greedy (GFG) routing extended with auctions are proposed in this paper. In this paper, we assume that all robots are mobile, and after the event is found (reported by sensors), the goal is to allocate the task to the most suitable robot to act upon the event, using either distance or the robots’ remaining energy as metrics. The proposed algorithms consist of two phases. The first phase of algorithms is based on face routing, and we introduced the parameter called search radius (SR) at the end of this first phase. Routing is considered successful if the found robot is inside SR. After that, the second phase, based on auctions, is initiated by the robot found in SR trying to find a more suitable one. In the simulations, network lifetime and communication costs are measured and used for comparison. We compare our algorithms with similar algorithms from the literature (k-SAAP and BFS) used for the task assignment. RFTA2 and RFTA2GE feature up to a seven-times-longer network lifetime with significant communication overhead reduction compared to k-SAAP and BFS. Among our algorithms, RFTA2GE features the best robot energy utilization.

With the ever advancing expansion of the Internet of Things (IoT) into our everyday lives, the number of attack possibilities increases. Furthermore, with the incorporation of the IoT into Critical Infrastructure (CI) hardware and applications, the protection of not only the systems but the citizens themselves has become paramount. To do so, specialists must be able to gain a foothold in the ongoing cyber attack war-zone. By organising the various attacks against their systems, these specialists can not only gain a quick overview of what they might expect but also gain knowledge into the specifications of the attacks based on the categorisation method used. This paper presents a glimpse into the area of IoT Critical Infrastructure security as well as an overview and analysis of attack categorisation methodologies in the context of wireless IoT-based Critical Infrastructure applications. We believe this can be a guide to aid further researchers in their choice of adapted categorisation approaches. Indeed, adapting appropriated categorisation leads to a quicker attack detection, identification, and recovery. It is, thus, paramount to have a clear vision of the threat landscapes of a specific system.

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Current trends focus on deployment of a single UAV or a swarm of it to generally map an area, perform surveillance, monitoring or rescue operations, collect data from ground sensors or various communicating devices, provide additional computing services close to data producers, etc. Applications are very diverse and call for different features or requirements. But UAV remain low-power battery powered devices that in addition to their mission, must fly and communicate. Thanks to wireless communications, they participate to mobile dynamic networks composed of UAV and ground sensors and thus many challenges have to be addressed to make UAV very efficient. And behind any UAV application, hides an optimization problem. There is still a criterion or multiple ones to optimize such as flying time, energy consumption, number of UAV, quantity of data to send/receive, etc.

The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.

Disaster scenarios are particularly catastrophic in urban environments, which are very densely populated in many cases. Disasters not only endanger the life of people, but also affect the existing communication infrastructures. In fact, such an infrastructure could be completely destroyed or damaged; even when it continues working, it suffers from high access demand to its limited resources within a short period of time. This work evaluates the performances of smartphones and leverages the ubiquitous presence of mobile devices in urban scenarios to assist search and rescue activities following a disaster. Specifically, it proposes a collaborative protocol that opportunistically organizes mobile devices in multiple tiers by targeting a fair energy consumption in the whole network. Moreover, it introduces a data collection scheme that employs drones to scan the disaster area and to visit mobile devices and collect their 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 and smart self-organization, thereby effectively assisting search and rescue operations.

Wireless networks are now a part of the everyday life of many people and are used for many applications. Recently, new technologies that enable low-power and long-range communications have emerged. These technologies, in opposition to more traditional communication technologies rather defined as "short range", allow kilometer-wide wireless communications. Long-range technologies are used to form Low-Power Wide-Area Networks (LPWAN). Many LPWAN technologies are available, and they offer different performances, business models etc., answering different applications’ needs. This makes it hard to find the right tool for a specific use case. In this article, we present a survey about the long-range technologies available presently as well as the technical characteristics they offer. Then we propose a discussion about the energy consumption of each alternative and which one may be most adapted depending on the use case requirements and expectations, as well as guidelines to choose the best suited technology.

In k-anycasting, a sensor wants to report event information to any k sinks in the network. In this paper, we describe KanGuRou, the first position-based energy efficient k-anycast routing which guarantees the packet delivery to k sinks as long as the connected component that contains s also contains at least k sinks. A node s running KanGuRou first computes a tree including k sinks with weight as low as possible. If this tree has m ≥ 1 edges originated at node s, s duplicates the message m times and runs m times KanGuRou over a subset of defined sinks. We present two variants of KanGuRou, each of them being more efficient than the other depending of application settings. Simulation results show that KanGuRou allows up to 62% of energy saving compared to plain anycasting.

This paper considers the problem of designing power efficient routing with guaranteed delivery for sensor networks with 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 HECTOR, 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 distance in tree coordinates. If such a node does not exist, then forwarding is made to the neighbor that reduces tree-based distance only and optimizes power cost over tree distance progress ratio. We theoretically prove the packet delivery and propose an extension based on the use of multiple trees. 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.

In this work we present a decentralized deployment algorithm for wireless mobile sensor networks focused on deployment Efficiency, connectivity Maintenance and network Reparation (EMR). We assume that a group of mobile sensors is placed in the area of interest to be covered, without any prior knowledge of the environment. The goal of the algorithm is to maximize the covered area and cope with sudden sensor failures. By relying on the locally available information regarding the environment and neighborhood, and without the need for any kind of synchronization in the network, each sensor iteratively chooses the next-step movement location so as to form a hexagonal lattice grid. Relying on the graph of wireless mobile sensors, we are able to provide the properties regarding the quality of coverage, the connectivity of the graph and the termination of the algorithm. We run extensive simulations to provide compactness properties of the deployment and evaluate the robustness against sensor failures. We show through the analysis and the simulations that EMR algorithm is robust to node failures and can restore the lattice grid. We also show that even after a failure, EMR algorithm call still provide a compact deployment in a reasonable time.

The last decades have witnessed advances in multiple wireless sensor networks in both the academic and industrial world. [...]

The Smart Grid (SG) aims to transform the current electric grid into a “smarter” network where the integration of renewable energy resources, energy efficiency and fault tolerance are the main benefits. This is done by interconnecting every energy source, storage point or central control point with connected devices, where heterogeneous SG applications and signalling messages will have different requirements in terms of reliability, latency and priority. Hence, data routing and prioritization are the main challenges in such networks. So far, RPL (Routing Protocol for Low-Power and Lossy networks) protocol is widely used on Smart Grids for distributing commands over the grid. RPL assures traffic differentiation at the network layer in wireless sensor networks through the logical subdivision of the network in multiple instances, each one relying on a specific Objective Function. However, RPL is not optimized for Smart Grids, as its main objective functions and their associated metric does not allow Quality of Service differentiation. To overcome this, we propose OFQS an objective function with a multi-objective metric that considers the delay and the remaining energy in the battery nodes alongside with the dynamic quality of the communication links. Our function automatically adapts to the number of instances (traffic classes) providing a Quality of Service differentiation based on the different Smart Grid applications requirements. We tested our approach on a real sensor testbed. The experimental results show that our proposal provides a lower packet delivery latency and a higher packet delivery ratio while extending the lifetime of the network compared to solutions in the literature.

Opportunistic communications present a promising solution for disaster network recovery in emergency situations such as hurricanes, earthquakes, and floods, where infrastructure might be destroyed. Some recent works in the literature have proposed opportunistic-based disaster recovery solutions, but they have omitted the consideration of mobile devices that come with different network technologies and various initial energy levels. This work presents COPE, an energy-aware Cooperative OPportunistic alErt diffusion scheme for trapped survivors to use during disaster scenarios to report their position and ease their rescue operation. It aims to maintain mobile devices functional for as long as possible for maximum network coverage until reaching proximate rescuers. COPE deals with mobile devices that come with an assortment of networks and aims to perform systematic network interface selection. Furthermore, it considers mobile devices with various energy levels and allows low-energy nodes to hold their charge for longer time with the support of high-energy nodes. A proof-of-concept implementation has been performed to study the doability and efficiency of COPE, and to highlight the lessons learned.