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Unmanned Aerial Vehicles (UAVs) are well-developed technologies that were first utilized for military applications such as border monitoring and reconnaissance in hostile territories. With the advancement of the Internet of Things (IoT) systems and smart mobile devices, several applications in various industrial, agricultural, smart homes, smart cities, smart transportation, etc. domains have emerged. These applications usually require broad coverage, high energy consumption, computation-intensive processing, and access to rich data gathered by sensor devices. UAVs’ inherent features such as high dynamicity, low deployment and operational costs, quick deployment, and line of sight communication have motivated researchers in the IoT domain to consider UAVs integration into IoT systems toward the notion of UAV-assisted IoT systems. In this paper, recent literature on UAV-assisted services in IoT environments is studied. A service-oriented classification is applied in order to categorize the presented schemes into four broad domains of UAV-assisted data-related services, UAV-assisted battery charging, UAV-assisted communications, and UAV-assisted Mobile Edge Computing (MEC). The literature belonging to each category is summarized with respect to their main points. Finally, some possible future directions are discussed to highlight the challenges associated with designing UAV-assisted IoT systems.

One of the main problems in the VANET（vehicular ad-hoc network）routing algorithms is how to establish the stable routes.The link duration in these networks is often very short because of the frequent changes in the network topology.Short link duration reduce the network efficiency.Different speeds of the vehicles and choosing different directions by the vehicles in the junctions are the two reasons that lead to link breakage and a reduction in link duration.Several routing protocols have been proposed for VANET in order to improve the link duration,while none of them avoids the link breakages caused by the second reason.In this paper,a new method for routing algorithms is proposed based on the vehicles trips history.Here,each vehicle has a profile containing its movement patterns extracted from its trips history.The next direction which each vehicle may choose at the next junction is predicted using this profile and is sent to other vehicles.Afterward each vehicle selects a node the future direction of which is the same as its predicted direction.Our case study indicates that applying our proposed method to ROMSGP（receive on most stable group-path）routing protocol reduces the links breakages and increases the link duration time.

Software-defined network (SDN) has had the evolution of the current network with the aim of removing its restrictions so that the data plane has been separated from its control plane. In the architecture of the SDN, the most controversial device is the OpenFlow Switch in that in the OpenFlow Switch, it is packets which are processed and investigated. Now, OpenFlow Switch versions 1.0 and 1.1 have been implemented on hardware platforms and support limited specifications of the OpenFlow. The present article is to design and implement the architecture of the OpenFlow v1.3 Switch on the Virtex® -6 FPGA ML605 board because the FPGA platform has high flexibility, processing speed and reprogrammability. Although little research investigated performance parameters of the OpenFlow Switch, in the present study, the OpenFlow system (switch and controller) is to be implemented on the FPGA via the VHDL on the one hand, and performance parameters of the OpenFlow Switch and its stimulation performance is to be investigated via the ISE design suite on the other hand. In addition to enjoying high flexibility, this architecture has a consumer hardware at the level of other start-ups. The main advantage of the proposed design is that it increases the speed of packet pipeline processing in flow tables switch. Besides, it supports the features of the OpenFlow v1.3. Its parser supports 40 packet headers in the network and provides the possibility of switch development for next versions of the OpenFlow as easily as possible.

With the advancement of the Internet of Things (IoT) and the changing needs of edge computing applications within the TCP/IP architecture, several challenges have emerged. One solution to these challenges is to integrate edge computing with information-centric networks (ICN). In ICN-based edge computing, there is a high level of similarity in request computing due to the proximity of users, which is leveraged to improve the efficiency of computation reuse. Computation reuse occurs through naming, caching, and forwarding. Computation reuse through forwarding means that similar requests are directed to the same compute node (CN). In many past works, forwarding algorithms for computation reuse have been used with high overhead for resource discovery or did not consider the important criterion of assessing the capacity of CNs. In this paper, we propose two forwarding algorithms, named TLCF) Trade-Off Between Load Balancing and Computation Reuse Forwarding) and AFCT (Adaptive Forwarding Considering Capacity Threshold), that measures criteria for selecting the best CN, the trade-off between computation reuse and load balancing, while also considering capacity. These two aspects lead to a reduction in completion time. Computation reuse inherently disrupts load balancing. The evaluation was conducted using the ndnSIM simulation. Through simulations, we have shown that our method significantly reduces completion time compared to the default method, achieving an improvement of approximately 22%. These findings highlight the efficiency and potential of our proposed method in optimizing edge computing performance.

In edge computing, repetitive computations are a common occurrence. However, the traditional TCP/IP architecture used in edge computing fails to identify these repetitions, resulting in redundant computations being recomputed by edge resources. To address this issue and enhance the efficiency of edge computing, Information-Centric Networking (ICN)-based edge computing is employed. The ICN architecture leverages its forwarding and naming convention features to recognize repetitive computations and direct them to the appropriate edge resources, thereby promoting “computation reuse”. This approach significantly improves the overall effectiveness of edge computing. In the realm of edge computing, dynamically generated computations often experience prolonged response times. To establish and track connections between input requests and the edge, naming conventions become crucial. By incorporating unique IDs within these naming conventions, each computing request with identical input data is treated as distinct, rendering ICN’s aggregation feature unusable. In this study, we propose a novel approach that modifies the Content Store (CS) table, treating computing requests with the same input data and unique IDs, resulting in identical outcomes, as equivalent. The benefits of this approach include reducing distance and completion time, and increasing hit ratio, as duplicate computations are no longer routed to edge resources or utilized cache. Through simulations, we demonstrate that our method significantly enhances cache reuse compared to the default method with no reuse, achieving an average improvement of over 57%. Furthermore, the speed up ratio of enhancement amounts to 15%. Notably, our method surpasses previous approaches by exhibiting the lowest average completion time, particularly when dealing with lower request frequencies. These findings highlight the efficacy and potential of our proposed method in optimizing edge computing performance.

The remarkable growth of cloud computing applications has caused many data centers to encounter unprecedented power consumption and heat generation. Cloud providers share their computational infrastructure through virtualization technology. The scheduler component decides which physical machine hosts the requested virtual machine. This process is virtual machine placement (VMP) which, affects the power distribution, and thereby the energy consumption of the data centers. Due to the heterogeneity and multidimensionality of resources, this task is not trivial, and many studies have tried to address this problem using different methods. However, the majority of such studies fail to consider the cooling energy, which accounts for almost 30% of the energy consumption in a data center. In this paper, we propose a metaheuristic approach based on the binary version of gravitational search algorithm to simultaneously minimize the computational and cooling energy in the VMP problem. In addition, we suggest a self-adaptive mechanism based on fuzzy logic to control the behavior of the algorithms in terms of exploitation and exploration. The simulation results illustrate that the proposed algorithm reduced energy consumption by 26% in the PlanetLab Dataset and 30% in the Google cluster dataset relative to the average of compared algorithms. The results also indicate that the proposed algorithm provides a much more thermally reliable operation.

Energy-efficiency in visual surveillance is the most important issue for wireless multimedia sensor network (WMSN) due to its energy-constraints. This paper addresses the trade-off between detection-accuracy and power-consumption by presenting an energy-aware scheme for detecting moving target based on clustered WMSN. The contributions of this paper are as follows; 1- An adaptive clustering and nodes activation approach is proposed based on residual energy of detecting nodes and the location of the object at the camera’s field of view (FoV). 2- An effective cooperative features-pyramid construction method for collaborative target identification with low communication cost. 3- An in-network collaboration mechanism for cooperative detection of the target is proposed. The performance of this scheme is evaluated using both standard datasets and personal recorded videos in terms of detection-accuracy and power-consumption. Compared with state-of-the-art methods, our proposed strategy greatly reduces energy-consumption and saves more than 65% of the network-energy. Detection-accuracy rate of our strategy is 11% better than other recent works. We have increased the Precision of classification up to 49% and 65% and the Recall of classification up to 53% and 71% for specific-target and object-type respectively. These results demonstrate the superiority of our scheme over the recent state-of-the-art works.

Connected devices in IoT continuously generate monitoring and measurement data to be delivered to application servers or end-users. Transmitting IoT data through networks would lead to congestion and long delays. NDN is an emerging network paradigm based on name-identified data known to be an appropriate architecture for supporting IoT networks. In-network caching is one of the main advantages of NDN, a major issue discussed in many studies. One of the significant challenges for some IoT data is the transiency nature, making the data caching mechanism different. IoT data such as ambient monitoring in urban areas and tracking current traffic conditions are often transient, which means these data have a limited lifetime and then expire. In the proposed approach, data placement is decided upon based on the data lifetime and node position. Data lifetime is an essential property that must be involved in caching methods; consequently, the data are classified based on the data lifetime, and specific nodes are selected for caching according to defined classes and nodes’ positions in topology. Based on the proposed scheme, the nodes with the highest outgoing interface count or the edge nodes are selected for data caching. By considering both data lifetime and node location, we determine the suitable caching location for each data class separately. In addition, we remove data that has a short lifetime and is not suitable for caching from the caching mechanism of NDN nodes. By considering both the cache and data placements for transient data, a more comprehensive view is grasped in improving the caching performance. This issue, which has not been addressed in the available studies run on IoT data caching, can lead to the appropriate use of available storage and reduce redundancy. Eventually, the simulation results performed by the ndnSIM simulator show the proposed method could improve the cache mechanism efficiency in terms of both delay and hit ratio. Comparison results of the proposed method with CE 2 and Btw indicate that this method can provide a reduction in average delay and an increase in cache hit ratio.

Distributed Video Coding with low computational complexity at the encoder side has a high potential for use in Wireless Multimedia Sensor Networks. However, the different architecture of this coding and resource constraints in WMSNs require the design of new efficient transmission protocols for transmission of DVC through WMSNs. In view of these protocols, error control mechanisms have more importance in reliable multimedia communication over WMSN. These mechanisms provide higher video quality in receiver nodes while saving the energy of sender nodes by the reliable transmission of packets. Given the importance of this issue, in this paper, we propose an adaptive, cross-layer error control scheme to protect video frames in the transmission of DVC over WMSN, which serves QOS while considering energy consumption and frames’ delay constraints. To propose this scheme, we used the accurate results from our previous works on error resiliency of DVC and comparative performance analysis of error control methods for this codec. The proposed scheme has been analyzed and compared to all standard, layer and, multi-layer error control schemes against the most important criteria in video communication over WSNs such as energy consumption, delay, and PSNR. Simulation results show that this scheme saves the quality of video in different channel conditions by consuming the least possible amount of energy based on the maximum allowable delay at the receiver.

Predicting the next movement directions, which will be chosen by the vehicle driver at each junction of a road network, can be used largely in VANET (Vehicular Ad-Hoc Network) applications. The current methods are based on GPS. In a number of VANET applications the GPS service is faced with some obstacles such as high-rise buildings, tunnels, and trees. In this paper, a GPS-free method is proposed to predict the vehicle future movement direction. In this method, vehicle motion paths are described by using the sequence of turning directions on the junctions, and the distances between the junctions. Movement patterns of the vehicles are extracted through clustering of the vehicle’s motion paths using SOM (Self Organizing Map). These patterns are then used for predicting the next movement direction, which will be chosen by the driver at the next junction. The obtained results indicate that our GPS-free method is comparable with the GPS-based methods, while having more advantages in different applications regarding urban traffic.