Our aim with this paper is to introduce a new protocol for self-organization and data-collection in heterogeneous wireless sensor networks (WSNs) called SDCP (Self-organization and Data-collection Protocol). This protocol is supported by two types of special mobile nodes (SƿN) that are rich in resources (those that can move by flying (such as Unmanned Aerial Vehicle - UAVs) and those that can move on the ground (Actors)). Through our SDCP protocol, we plan to ensure optimal performance measures, such as energy efficiency, low latency, high success rate, a moderate number of hops, and suitably interactions between static sensors and SpNs while benefiting from their mobility (in a hierarchical heterogeneous network with unstable architecture due to mobility). The SƿN moves conforming to a particular mobility model, namely random waypoint mobility model, which allows pause time. Each SƿN, while it is suspended, forms a transitory cluster that heads for collecting and processing sensed data (sensed by static sensor nodes in that cluster). During his trip, SƿN piggybacks the collected data and returns it to a base station (BS). Once the data transferred to the BS, SƿN continues its movement again for a new data collection. The simulations carried out, delighted us with valuable performance outcomes.

Research into vision-based obstacle detection systems plays a fundamental role in developing autonomous vehicles and intelligent transportation systems. In fact, an intelligent vehicle should be ready to discover vehicles and possible obstacles along its route. This paper presents a comparative study of existing state-of-the-art methods for obstacle detection. However, there have been a large number of studies that thoroughly explored various types of state-of-the-art methods for obstacle detection. Here, this paper compares three methods in obstacle detection, namely the “Robust obstacle detection for ADAS using distortions of IPM of a monocular camera”, “Robust Obstacle Detection and Recognition for DAS”, and “Real-time Obstacle Detection Over Rails Using Deep CNN” and analyzes the obtained results. 

The main purpose for wireless sensor networks is especially for data collection. Nevertheless, since the sensors are mostly immobile, the operation of transferring data they captured to the base station is a critical task since the exchange environment is unstable. Our approach consists in injecting mobile relay nodes whose mission is mainly to maximize the lifetime of the network, also to provide connectivity between any sensor and the base station, and to minimize the latency of receiving data sent to the base station. Smart mobility is the key for our approach to ensure reliability by forming a virtual mobile circle using relay nodes. We have proved formally and by simulation the effectiveness of our approach, the obtained results prove the efficiency of our approach.

Mobile Heterogeneous Wireless Sensor Networks (Heterogeneous WSNs) are mainly characterized by their internal diversity. In such networks, the variety of properties in each component provides profitable outcomes related to many metrics such as network lifetime and hardware cost. Although it offers remarkable advantages, random mobility causes major difficulties in the management of Heterogeneous WSNs. The purpose of this paper is to introduce a controlled mobility approach for Heterogeneous WSNs using Unmanned Aerial Vehicles (UAVs). According to experiments, the proposed deterministic and genetic methods can efficiently deal with the complexity of Mobile Heterogeneous WSNs compared to the previously applied random strategies. The obtained results prove that our suggested techniques can achieve a greater delivery ratio, a faster coverage time, and a faster latency.

In recent years, there has been rapid development in the research area of the car's driving assistance. Deep learning was used to solve different problems, such as object detection, visual recognition, speech recognition and handwriting recognition and was achieved a very good performance. In deep learning, Convolutional Neural Networks (ConvNets or CNNs) are found to give the most accurate results, in solving object detection problems. In this paper, we'll go into summarizing some of the most important deep learning models used for object detection tasks over this last recent year, then we present an approach where we detect roadsides, then we seek objects located on the road area to prevent driver. As the state of the driver is very important information, we try to detect driver's drowsiness. We use a camera with an algorithm to calculate the eye aspect ratio. Finally, we evaluated the three modules of proposed system using our collected data-set.

Mobile Wireless Sensor and Actor Networks (MWSANs) can simply be defined as an extension of Wireless Sensor and Actor Networks (WSANs) in which the actor nodes are mobile. As such, in addition to challenges existing in WSAN, the mobility also imposes new challenges such as localization of actors, cooperative tracking of both actor-actor and actor-sensor collaboration, and communication infrastructure between distant actors. New communication protocols, specific to MWSANs, are needed. In this paper, we propose a self-organization and data collection protocol in order to provide energy efficiency, low latency, high success rate and suitably interactions between sensors and actors and take benefit from the mobility and resources existing on the network's actor nodes. The actor nodes move according to RWP mobility model. Each actor, during its pause time creates a temporary cluster, and is the head of it, collects and processes sensor data and performs actions on the environment based on the information gathered from sensor nodes in its cluster. Once an actor detects a base station it delivers the collected data to it. The simulations carried out (with TOSSIM tool), comfort us with good performances results.

In this paper, we propose a novel self-organization protocol for wireless sensor networks (WSNs) assisted by Unmanned Aerial Vehicle (UAV or Drone) called SSP (Self- organization Smart Protocol). In order to provide energy efficiency, low latency, high success rate and suitably interactions between sensors and UAVs while taking advantage of the air mobility (fly) and resource available on the UAVs in the network. The UAVs move according to RWP (Random Waypoint) mobility model. Each UAV, during its pause time at a known height, creates a temporary cluster, and acts as its head, collects and processes sensor data and performs actions on the environment based on the information gathered from sensor nodes in its cluster. Once an UAV detects a base station (BS) it forwards the collected data to it. The results of the simulations show the high performance of the proposed algorithm

The mobility of nodes in a wireless sensor network is a factor affecting the quality of service offered by this network. We think that the mobility of the nodes presents an opportunity where the nodes move in an appropriate manner. Therefore, the routing algorithms can benefit from this opportunity. Studying a model of mobility and adapt it to ensure an optimal routing in an agitated network is the purpose of our work. We are interested in applying a variant of the mobility model RWP (named routing-random waypoint 'R-RWP') on the whole network in order to maximise the coverage radius of the base station (which will be fixed in our study) and thus to optimise the data delivery end-to-end delay.

The main purpose for wireless sensor networks is especially for data collection. Nevertheless, since the sensors are mostly immobile, the operation of transferring data they captured to the base station is a critical task since the exchange environment is unstable. Our approach consists in injecting mobile relay nodes whose mission is mainly to maximize the lifetime of the network, also to provide connectivity between any sensor and the base station, and to minimize the latency of receiving data sent to the base station. Smart mobility is the key for our approach to ensure reliability by forming a virtual mobile circle using relay nodes. We have proved formally and by simulation the effectiveness of our approach, the obtained results prove the efficiency of our approach.

Mobility has always represented a complicated phenomenon in the network routing process. This complexity is mainly facilitated in the way that ensures reliable connections for efficient orientation of data. Many years ago, different studies were initiated basing on routing protocols dedicated to static environments in order to adapt them to the mobile environment. In the present work, we have a different vision of mobility that has many advantages due to its 'mobile' principle. Indeed, instead of searching to prevent mobility and testing for example to immobilize momentarily a mobile environment to provide routing task, we will exploit this mobility to improve routing. Based on that, we carried out a set of works to achieve this objective.

For our first contribution, we found that the best way to make use of this mobility is to follow a mobility model. Many models have been proposed in the literature and employed as a data source in most studies. After a careful study, we focused on the Random Waypoint mobility model (RWP) in order to ensure routing in wireless networks. Our contribution involves a Random Waypoint model (in its basic version) that was achieved on the TOSSIM simulator, and it was considered as a platform for our second (and main) contribution, in which we suggested an approach based RWP where network nodes can collaborate and work together basing on our recommended algorithm. Such an approach offers many advantages to ensure routing in a dynamic environment. Finally, our contributions comprise innovative ideas for suggesting other solutions that will improve them.

Since our research to study mobility, we noted that the best way to represent the mobility of sensor nodes is to follow a mobility model. Numerous models have been proposed in the literature, they are commonly used as a data source in all kinds of studies, including motion in several networks. To this end, we propose to exploit this mobility, which results in the proposal of a new approach based on a mobility model to ensure routing in networks of mobile and wireless sensors. Inspired from a powerful mobility model (i.e. Random Waypoint), our approach offers many advantages for routing in a dynamic environment based on the collaboration of sensor nodes, and thus to conclude for new and innovative ideas.

Intermittently connected mobile networks are wireless networks, where, often, there is no complete path between the source and destination nodes and, the network is considered as a set of disconnected groups. This definition is related to Delay Tolerant Networks (DTN). Thus, several studies have been performed to design routing protocols that consider substantially delay tolerance constraint. In this paper, we investigate the projection of spray and wait routing protocol, which is dedicated to DTNs environments, in Wireless Sensor Networks (WSNs). The obtained results from the intensive performance evaluation of our proposal prove obviously the efficiency of spray and wait protocol in WSNs.

The main purpose of this paper is to enable congestion prediction on
urban freeways and objectively measure their impact on daily life.
Indeed, cars’ movement becomes increasingly difficult and, often
causes traffic jams that can last for hours, because of growing number
of used cars, in addition to the unforeseen events that may complicate
more and more this problem (weather, accidents, road works ...). This
is mainly due to the lack of efficient management of roads'
congestions. This paper attempts to bring a technological response to
significantly reduce the congestion problem. Our approach uses smart
sensors which allow traffic jam detecting and relaying such critical
information. Thus, while providing the drivers with context aware
information such as that related to the status of the highway before
crossing, we enable them to make decision and select another road
avoiding the congested one and reducing, by the way, the congestion
itself.

The main purpose of this paper is to enable congestion prediction on urban freeways and objectively measure its impact on daily life. Indeed, cars’ trafic becomes increasingly difficult and, often causes traffic jams that can last for hours, because of growing number of used cars, in addition to the unforeseen events that may complicate more and more this problem (weather, accidents, road works...). This is mainly due to the lack of efficient management of roads' congestions. This paper attempts to bring a technological response to significantly reduce the congestion problem. Our approach is based on smart sensors which allow traffic jam detecting and relaying such critical information. Thus, while providing the drivers with context aware information such as those related to the highway status before crossing, we enable them to make decision and select another road avoiding the congested one and reducing, by the way, the congestion itself.

Many solutions have been developed for routing
information in ad hoc networks. However, few of them are effective
when the topology is very dynamic. Indeed, building a routing table,
finding and maintaining a path or location of a node is a big
challenge when mobility is high. Currently this topic attracts the
attention of several research groups including those interested in
the vehicle networks (VANET), a special case of dynamic networks.
The VANET will improve road safety and develop new services to
drivers or passengers, such as access to Internet for example. In
this paper, we present a new approach for routing in very dynamic
networks based on communication conditions. Instead of transport
addresses (or positions), a message including certain information is
sent with certain conditions to ensure sustainability of optimal link
between the nodes of such a network.