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 objective of this work is to present an analysis of RPL Routing protocol for Low-Power and Lossy Networks (LLNs) in the Internet of Things (IoT) and to determine the adequate working mode between storing and non-storing. Objects in LLNs networks have limited processing power such as battery and memory. The study examined the behavior of RPL towards some number of quality of service parameters in Wireless Sensor Networks (WSNs). Simulations are conducted under ContikiCooja Simulator. Results show that non-storing mode has better results in terms of delay and loss rate, but the storing mode consumes less energy.

The interest shown by some community of researchers to autonomous drones or UAVs (Unmanned Aerial Vehicles) has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we have proposed a bio-inspired routing protocol for UAVs called BR- AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. The performances of BR-AODV were evaluated and compared to those of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.

Recent advances in wireless communication have led to the introduction of a novel network of miniaturized, low power, intelligent sensors that can be placed in, on, or around the body. This network is referred to as Wireless Body Area Network (WBAN). The main purpose of WBAN is to physiologically monitor patient's vital signs and consequently route the related data towards a base station. Since the environment of such a network is principally the human body, data routing mechanisms used in traditional wireless networks (e.g. WSN, WANET) need to be revised, and more restrictions have to be addressed in order to adapt it to WBAN routing challenges. Compared to those dedicated to on-body WBAN, in-body WBAN routing protocols have more constrains and restrictions and are expected to be efficient and robust. As better as we know, only few routing protocols have been proposed in literature and the research field stills underexplored. Therefore, in this paper we present an overview of the main existing routing protocols proposed for wireless in-body sensor networks (WIBSN).

Nowadays, wireless sensor networks (WSNs) are becoming challenging issue and used in many fields and applications. WSNS are characterized by many constraints as low energy, low computation capability and weak transmission range. The cloud computing is promising technology that can provide a flexible storage and processing infrastructure to process WSN's data in large scale. The two technologies (i.e.: WSNs, Cloud Computing) have gained significant research interest and their combining is a fundamental challenge that certainly will resolve many issues related to WSNs. In this paper we discuss firstly the idea of integrating WSNs in Cloud Computing starting with a state of the art analysis of existing approaches in this field. The second part of this paper is devoted to a comparative study between two efficient energy protocols that are proposed for WSNs.

WSNs consist of large number of small sensors densely deployed to monitor a phenomenon. Most of the data generated from the WSNs represent events happening at time intervals. Sometimes and according to the nature of the applications, this data stream is continuous and can reach high speeds. Therefore, adopting new techniques, platforms and tools to deal with this large amount of sensory data became necessary. Therefore, the Big Data paradigm can represent a good solution for the extraction, analysis, viewing, sharing, storage and transfer of such volume of data. This paper presents a survey on integrating Big Data tools for gathering, storing and analyzing the data generated by WSNs.

Data aggregation has emerged as a basic concept in wireless sensor networks. By combining data and eliminating redundancy, this concept showed its effectiveness in terms of reducing resources consumption. The data aggregation problem is not new and has been widely studied in wireless sensor networks. However, most of the proposed solutions are based on a static data aggregation scheme and do not consider the network constraints evolution such as energy bandwidth, overhead and transmission delay. To dynamically optimize the tradeoff between the data aggregation process and the network constraints, a new paradigm has been introduced recently named the feedback control system. The main idea is to adapt the data aggregation degree to the environment changes and the sensor network applications. The feedback control for data aggregation remains relatively a new research area and only few works have introduced this concept. Therefore this work points out this research field by surveying the different existing protocols.

Mobile ad hoc networks are characterized by infrastructure less, dynamic topology, without any centralized administration, and limited resources witch make more difficult the stream of multimedia applications over wireless networks. Providing Quality of Service for video streaming is an important challenge. In this paper, firstly, we have reviewed many issues and different coding techniques for video streaming over MANET and secondly we propose to study two routing protocols (AODV and DSDV) to evaluate witch of them can improve QoS for real-time multimedia applications. Results show that AODV protocol performs better than DSDV in terms of throughput and network load with high mobility, but roles are reversed in terms of bit rate, loss rate and network load for large-scale networks.