The communication at millimeter wave (mmWave) offers a very promising future for vehicular network in beyond fifth generation (B5G), where the high-throughput data exchange is required. However, due to the high mobility and channel attenuation in vehicular environment, it becomes more challenging to establish an efficient mmWave Vehicle-To-Everything (V2X) communication. To overcome the beam misalignment problem, it is necessary to employ fast and reliable beamforming methods at the transmitter and receiver sides to align the beams reliably and continuously. In this paper, we propose a new deep learning approach based on beam alignment that suggests a bidirectional long short term memory (BiLSTM) model followed by a fully connected layer, to predict the suitable beam pair in real time, i.e., the Angle of Departure (AoD) at the mmWave base station side and Angle of Arrival (AoA) at vehicle side (AoD/AoA) for which the received signal power is maximized. The BiLSTM-BPP is performed to identify automatically the strongest beam pair that connects the mmBS to the vehicle for each vehicle position. After a set of simulations, the obtained results showed that BiLSTM-BPP achieves the lowest beam prediction error between the predicted and the real AoD/AoA compared to existing traditional machine learning algorithms, including linear regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), decision tree, random forest. We evaluated the performance of our proposal in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE) and Root Mean-Square-Error (RMSE).

Despite the huge development in the fifth generation (5G) vehicular networks, the data transmission is still suffering from the signal attenuation, especially when vehicles move. It is due to the limited 5G connectivity range where the frequency waves, named mmWaves, are only able to cross a short distance and are interrupted by physical obstacles like buildings, trees, and walls. These obstacles can obstruct, disrupt or absorb a part of the transmitted signal. To overcome this drawback, various beam management approaches were proposed in the literature to find the best transmission beam that increases the performance of 5G vehicular networks. In this paper, we propose a new simulated annealing algorithm that adjusts dynamically the direction of the beam according to the connected vehicle positioning, where the mmWave base station selects for each vehicle position the optimal angle that maximizes the Signal-to-Interference-plus-Noise Ratio (SINR) of the received signal. The results obtained showed the effectiveness of this proposal to establish an efficient communication between the mmWave BS and the connected vehicle compared to conventional method.

These days, the most current disruptive trend in the auto industry is not “electric engines” but specifically the data collection engine that many cars will be equipped with in the future. One of the most glamorous areas that present many challenges as we progress is transport data sources. As we know, data becomes more useful the more it's used, which is the exact opposite of what happens with the Basic Safety Messages (BSM) exist only temporarily, used locally and are never stored. However, this frequently generated data by connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. In this paper, we propose a data reuse model that retains collected BSMs, stores and processes them inside the vehicle to provide a continuous data source containing stored snapshots for every stretch of roadway. These continuous data could be sent to roadside DSRC units, streamed over digital cellular, Wi-Fi, or through a combination of all to provide broad-scale applications with needed information. The main novelty of our reusing model is its contribution to change the way BSM is treated, making it more valued, resulting in a reliable and useful data source. Moreover, given that our model relies on In-Vehicle Computing (IVC) paradigm, its strongest potential is, therefore, its capability of facilitates data processing at or near the source of data generation while optimizing bandwidth and reducing latency to rapidly provide data of value to drivers, pedestrians and transportation agencies.

Recently, autonomous vehicles navigation (AVN) attracted many researches trying to improve road traffic without the human intervention. One of the main challenges in AVN is allowing a vehicle to discover its moving trajectory with a reduced computational complexity. To cope with this issue, we propose in this paper a new simulated annealing algorithm to discover an optimal trajectory when the vehicle encounters an obstacle using LiDAR perception. The found trajectory is then sent to a roadside unit (RSU), which communicates this discovery to other nodes in the network for further use. During its navigation, the vehicle perceives the environment by a LiDAR sensor to detect an eventual obstacle and launches an optimal path discovery to reach the final destination in a reduced time. The results obtained showed the effectiveness of our proposal to find an optimal route compared to Dijkstra algorithm.

Communication security in Cluster-Based Wireless Sensor Network (CWSN) is considered as a challenging task facing this type of network. The dynamic nature of CWSN and its deployment in open areas making these networks vulnerable to different kinds of cyber-attacks that can adversely affect its own functioning. Moreover, the resource-constrained nature of sensor devices make it impractical to apply conventional security schemes in CWSN, which require high overhead of computation, communication and memory storage. To deal with this issue of communication security, we propose an efficient secure protocol in CWSN called IDentity-based Security Protocol (IDSP), which guarantees secure communication between Cluster Head (CH) and Base Station (BS), as well as communication between CH and Sensor Node (SN). IDSP is based on Identity-Based Cryptography (IBC) that doesn't require Public Key Infrastructure (PKI) or complicated certificate management. The cornerstone of our protocol is to provide a perfect trade-off between three key factors: (i) ensuring a good protection level, especially against various cyber-attacks that may target CWSN, (ii) proposing an efficient process regarding energy consumption and key storage, (iii) introducing a key exchange mechanism in IDSP which is lightweight and uncomplicated. Moreover, our protocol treats public key authentication problem. The proposed protocol is tested in Cooja network simulator with WiSMote platform, and the performance results showed that IDSP is efficient, lightweight and secure.

Device-to-Device (D2D) communications is a convenient technology for future cellular network. This type of communication helps offload network core and increases overall throughput. Among the many challenges facing D2D communication, we are interested in interference management. In order to decrease interferences and improve network throughput, we need to optimize resource allocation and power control for D2D communication. Therefore, we propose in this paper a new Bees Life Algorithm (BLA). This algorithm is considered as a bioinspired approach to optimize channel allocation and power control. BLA is responsible for finding the optimal solution via a diversified global search among all possible solutions. The purpose of all this is to escape a stagnation on local optimal by applying a biological process inspired by the bees' marriage behavior. Additionally, BLA performs an intensified local search to get the optimal solution. We consider a cellular network with a set of users, cellular users and D2D pairs that share resource blocks. A minimum signal to interference plus noise ratio (SINR) is considered for cellular users, and there is no limit to the number of D2D pairs that share the same resource block. The experimental study showed that BLA provides good results in terms of overall throughput after comparison against two other bio-inspired approaches namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).

During recent years, a body area network is becoming an important tool to improve the healthcare by monitoring patient's health state at distance, at home, in work, or when traveling. This body sensor-based network is easy to use, and available with low cost into two types; the first one can be swallowed or implanted under the skin where the second type is wearable, these became available within the Internet of Things (IoT). Physiological sensor-based systems have been recently designed to detect the emotional stress. By this way, we propose in this paper, a new monitoring system for driver stress detection based on an enhanced random forest classification approach. This proposal analyses and monitors driver electrocardiogram (ECG) signal when driving in order to discover its stress state belonging to one of the following three levels namely, low, medium or high. This proposal could help to detect and diagnostic stress level and alert the driver, its family and the other road users to avoid accidents caused by high stress state. The proposed system suggests the integration of a simulated annealing algorithm to enhance the random forest classification method in order to reach the highest classification accuracy. According to various drivers' ECG acquired from MIT-BIH physioNet dataset, the experimental study showed that the proposed random forest algorithm outperforms support vector machine (SVM) classification method to detect driver stress levels in terms of recognition accuracy.

Cardiac diseases are one of the foremost reasons of mortality in the worldwide. To cope with this issue, cardiology doctors insist on the early detection of cardiac diseases often with the use of an electrocardiogram (ECG) signal, providing timely and appropriate treatment for heart patients. In the literature, there are many efficient classification approaches like random forest method, conceived for ECG signal analysis to detect cardiac diseases. However, the execution of random forest requests introducing manually the number of trees as a parameter user, which is considered as a major drawback of this method, since often the user did not find the optimal tree value. In this paper, we propose to enhance the random forest method by suggesting a new simulated annealing (SA) algorithm to find the optimal number of trees where the accuracy of classifying the ECG signal is tackled as an objective function. The proposed system involves four main steps namely data collecting of ECG signal, pretreatment and denoising this data, feature extraction and classifying this signal using the enhanced random forest approach. To validate this proposal, a set of experiments was conducted on the well-known European Physionet ST-T and MIT/BIH databases as well as the USA Heart Disease Data Set and Arrhythmia Data Set of UCI machine learning repository. The results obtained showed that the enhanced random forest can reach 99.62% of classification accuracy according to the optimal found number of trees.

In the U.S, the Basic Safety Message (BSM), commonly known as heartbeat message, has been proposed as the primary message set used by the connected vehicles' safety applications to constantly exchange data within the vehicular ad hoc networks (VANETs). BSM is communicated around 10 times per second through Dedicated Short-Range Communications (DSRC) to be exclusively used in the safety context. Once the BSM is used, it becomes obsolete and is destroyed. Nevertheless, albeit the BSM has been primarily developed to be used in the safety context, it has a capacious field of use. Other connected vehicle non-safety applications, like efficiency, environment, weather and mobility, may also use the data in the message. A wide range of BSM data elements are required by an important bunch of non-safety applications. Leastwise, core data elements of the BSM part one if cached, grouped and sent otherwise, they can solely and adequately provide the vehicle-based information needed for broad-scale applications. In this paper, we propose an Application-Level Flextime Aggregation (ALFA) scheme, allowing BSMs to be cached on-board the vehicle, intelligently aggregated according to different contexts and then sent in new message containing stored snapshots for every stretch of roadway. These new messages could be sent to vehicles and infrastructures, via Wi-Fi, digital cellular, or over a combination of all to provide broad-scale applications with needed information. The main novelty of our ALFA scheme is its contribution to change the way active safety transportation data (BSM) is treated, making it more valued, resulting in reliable and useful data source, and an efficient transportation network scaling to large areas. After a study case relevant to the vehicles mobility, obtained results showed that ALFA enhances the BSM' usefulness and provide mobility applications with useful input data.

One of the most important source of WSN vulnerability is the forwarding process between the nodes. In fact, routing protocols are often based on clustering principle, where a set of clusters represented by a Cluster Head (CH) for each cluster, is conceived to rely fundamentally data packets between the network nodes. These protocols are vulnerable to several attacks affecting different routing processes such as hello flooding, selective forwarding, replay process. These attacks are more damaging if the CH is corrupted by a malicious node playing CH role. This paper proposes a new secure protocol based on the well-known LEACH routing protocol named Hybrid Cryptography-Based Scheme for secure data communication in cluster-based WSN (HCBS). As a multi-constrained criteria approach, HCBS is built on a combination of the cryptography technique based on Elliptic Curves to exchange keys that uses symmetric keys for data encryption and MAC operations. After a set of tests on TOSSIM simulator, the results obtained showed that our proposal achieves good performances in terms of energy consumption, loss rate and end-to-end delay. In addition, HCBS guaranties a high level of security.

Research and development on video streaming over vehicular ad hoc networks (VANETs) have expanded rapidly in the last few years. In order to improve road safety and to satisfy road users requirements, video streaming has been proposed to disseminate continuously, an accurate video data, concerning traffic circumstance, travel information, divertissement, etc. High quality video streaming in vehicular environment is very challenging due to the very high dynamic of VANET topology and its intermittent connection, stringent requirements of video transmission such as reduced propagation delay and decreased signal to noise ratio. In this paper, we propose a new protocol called Enhanced User Datagram Protocol (EUDP) for video streaming in VANET. Unlike User Datagram Protocol (UDP) which did not consider any recovery mechanism of erroneous packets, EUDP uses Sub-Packet Forward Error Correction (SPFEC), and adopts the unequal protection of video frame types (i.e. I, P, B) to improve the video streaming quality. After a set of simulations and comparisons with UDP and EUDP without unequal protection of video frame types (EUDP-E), our proposal showed a significant improvement in terms of error recovery rate, PSNR and MOS of transmitted video.

Advancements in communications technologies are spurring a revolution within the United States transportation network. Short-range dedicated radio allows vehicles to communicate with each other within VANET. Up to date, tremendous efforts from government, academia and industry have made progress in designing and implementing several vehicular safety applications. Therefore, significant efforts have been made in designing lower-layer communication protocols for VANET. Also, Connected Vehicles (CV) concept is moving rapidly from the experimental phase into real-world deployments. Nevertheless, since all vehicles in range are shown as destination nodes, safety applications in VANET critically need an optimized way for effectively transmitting information to surrounding vehicles. To respond to this need, we propose in this paper a new Request-To-Receive (RTR) approach as a crucial step in the Basic Safety Messages (BSM) transmission process among vehicles incubating multiple safety applications. A new Data Element is proposed to be added to the basic safety messages data dictionary. Simulation results showed that RTR reduces the average number of transmitted Data Element when compared to the current situation.

Video streaming over vehicular ad hoc network (VANET) provides accurate information about road traffic situation, digital services requested by drivers and passengers, compared to textual messages. Video dissemination in VANET is considered as a hard task because of high dynamic topology of vehicles, stringent requirements of video like real time transmission and the volatility of wireless medium channels. In this paper, we propose a new video streaming scheme called enhanced adaptive sub-packet forward error correction (EASP-FEC) aiming to improve video transmission quality in VANET. Unlike existing packet forward error correction (PFEC) mechanisms proposed for video streaming in VANET, which generate redundant packets for each block of original packets, EASP-FEC divides a packet into a set of original sub-packets, then it generates redundant sub-packets for each packet, to enhance the error recovery rate and video streaming quality. EASP-FEC also avoids the network congestion problem compared to sub-packet forward error correction (SPFEC) mechanism. We propose to apply EASP-FEC at the sender and relay vehicles, where the calculation process of redundant sub-packets take in consideration the traffic condition, the traffic load and the importance of video frame types (I, P, B). A set of simulations proved that EASP-FEC provides better error recovery rate than PFEC and avoids network congestion against SPFEC.

Abstract Vehicular ad hoc networks (VANETs) are witnessing in recent years a rapid development for road transmissions and are considered as one of the most important types of next generation networks, in which drivers can have access anywhere and anytime to information. However, vehicles have to deal with many challenges such as the links failures due to their frequent mobility as well as limited degrees of freedom in their mobility patterns. In this paper, we propose a new quality of service multicast and multipath routing protocol for VANETs, based on the paradigm of bee's communication, called multicast quality of service swarm bee routing for VANETs (MQBV). The MQBV finds and maintains robust routes between the source node and all multicast group members. Therefore, the average end‐to‐end delay and the normalized overhead load should be reduced, while at the same time increasing the average bandwidth and the packet delivery ratio. Extensive simulation results were obtained using ns‐2 simulator in a realistic VANET settings and demonstrated the efficiency of the proposed protocol. Copyright © 2013 John Wiley & Sons, Ltd.

Vehicular ad-hoc networks (VANETs) can be considered as complex dynamic systems where simulation modeling plays a crucial role in conducting research due to the difficulties in the realization of such a network, like high costs, risks and dangers. During the last decade, the results of VANET simulations were considered successful and acceptable however, most prior VANET simulators were based on non realistic mobility when describing node positions, distribution, frequency and movement, model paths, traffic signals etc. To deal with this challenge, we propose in this paper a Markov-History based modeling for realistic mobility of vehicles in VANETs. It starts with a geographic area digitization, followed by a vehicle positions initialization. The proposed model predicts directions of the vehicles using a Markov chain, as well as it predicts the vehicle velocities using a History-based sub-model. Moreover, the density of the network is updated according to the peak or off-peak hours. The emerged model can be used as mobility model to simulate VANET scenarios. To validate our proposal, a concrete mobility model has been designed using the proposed model that represents the downtown of Biskra City located in Algeria. The performances of the resulting network have been proved against the others obtained using Random Waypoint as a standard mobility model in terms of packet delivery ratio, network overhead, average end-to-end delay, and dropped packets.

Cloud computing is known as services delivery such as shared resources, platforms, software and data, in the interest of end-users. They are located in distributed datacenters over a network such as the Internet. In this paper a new cloud computing model called ITS-Cloud applied to the Intelligent Transportation Systems (ITS) is proposed to improve transport outcomes such as road safety, transport productivity, travel reliability, informed travel choices, environment protection, and traffic resilience. It consists of two sub-models: the statistic and the dynamic cloud sub-models. In the former, vehicles benefit of the conventional cloud advantages however; the dynamic one which is a temporary cloud is formed by the vehicles themselves which represent the cloud datacenters. To validate our proposal, a simulation study is performed to deal with the load balancing as a NP-Complete problem. The reached results are obtained using Bees Life Algorithm (BLA) applied to ITS-Cloud and compared with those reached by (BLA) applied only to the conventional Cloud.

Mobile ad hoc network (MANET) is a wireless network that consists of a large number of mobile and heterogeneous nodes interconnected by wireless links which move in an arbitrary direction with random velocity. It is characterized by the absence of any pre-existing infrastructure. This makes the transmission more difficult especially, in the case of multicast mode. Some of the most important applications that use the MANETs are the multimedia and real time systems. They require a very high level of quality of service (QoS) to make the network useful. In this paper, we propose a new QoS multicast routing protocol called MQBM for MANETs inspired by the bees' communication. It allows finding routes between the source and the head of multicast group responsible to communicate the packet toward the group members. Each node is allowed to transmit data only if the average end-to-end delay and the average bandwidth satisfy the QoS requirements. Extensive simulation results obtained using NS2 simulator, demonstrate the efficiency and the performance of the proposed protocol after comparisons against MAODV in terms of the average end-to-end delay and the average bandwidth as QoS metrics.

Research and industries are recently more interesting and attracting to the Vehicular Ad hoc Networks (VANETs) development domain. They contribute to safer and more efficient roads by providing timely and accuracy information to drivers and authorities. Thus, the definition of a quality of service routing protocol for VANETs is one of their challenges. In this paper, we propose QoSBeeVanet, a new quality of service multipath routing protocol adapted for the vehicular ad hoc networks. It is based on ideas of the autonomic bee communication. Simulation results taken with NS2 in realist urban settings were shown that QoSBeeVanet outperforms DSDV and AODV two of the current state-of-the-art protocols, in terms of end-to-end delay, packet delivery ratio, and normalized overhead load.