Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review

A survey of UAV-based data collection: Challenges, solutions and future perspectives

Unmanned aerial vehicle (UAV) path planning plays an important role in the flight process of an UAV, which needs an effective algorithm to deal with UAV path planning problem. The search and rescue optimization algorithm (SAR) is easy to implement and has the characteristics of flexible, but it has slow convergence speed and has not been applied to UAV path planning. To address these problems, a heuristic crossing search and rescue optimization algorithm (HC-SAR) is proposed. HC-SAR combines a heuristic crossover strategy with the basic SAR to improve the convergence speed and maintain the population diversity in the optimization process. Furthermore, a real-time path adjustment strategy is proposed to straighten the UAV flight path. In addition, cubic B-spline interpolation is used to smooth the generated path. Comprehensive experiments including two-dimensional and three-dimensional environments for different threat zone are conducted to validate the performance of HC-SAR. The results show that HC-SAR has a high convergence speed and can successfully obtain a safe and efficient path, and it significantly outperforms SAR, differential evolution (DE), ant lion optimizer (ALO), squirrel search algorithm (SSA) and salp swarm algorithm (SSA) in all the cases. These results suggest that the proposed algorithm can effectively serve as an alternative for solving UAV path planning problem. 

A novel UAV path planning approach: Heuristic crossing search and rescue optimization algorithm

Improving the communication of Internet of Things (IoT) network is a challenging task as it connects a wide-range of heterogeneous mobile devices. With an extended support from cloud network, the mobile IoT devices demand flexibility and scalability in communication. Increase in density of communicating devices and user request, traffic handling and delay-less service are unenviable. This manuscript introduces genetic algorithm based adaptive offloading (GA-OA) for effective traffic handling in IoT-infrastructure-cloud environment. The process of offloading is designed to mitigate unnecessary delays in request process and to improve the success rate of the IoT requests. The fitness process of GA is distributed among the gateways and infrastructure to handle requests satisfying different communication metrics. The process of GA balances between the optimal and sub-optimal solutions generated to improve the rate of request response. Experimental results prove the consistency of the proposed GA-OA by improving request success ratio, achieving lesser complexity, delay and processing time.

Genetic algorithm based adaptive offloading for improving IoT device communication efficiency

Wi-Fi Direct Research ‐ Current Status and Future Perspectives

An optimized UAV trajectory planning for localization in disaster scenarios

Device-to-Device communication is an essential component in the evolution towards the Internet of Things. The relatively new WiFi-Direct standard allows devices to communicate directly over the well established IEEE 802.11 protocol without the need for an intermediate access point. The increased range and bandwidth advantage of WiFi-Direct over existing short-range technologies such as Bluetooth and Zigbee allows a whole new class of applications to benefit from cooperation. Within a WiFi-Direct network, one device is elected as the group owner and acts as a central hub for all communications. The standard does not dictate a group owner selection strategy and leaves the decision to be taken at the application layer. In this work, we present an optimized strategy for group owner selection within WiFi-Direct networks that aims at maximizing the overall network performance in terms of increased throughput. Moreover, we propose a low complexity multi-device group owner negotiation protocol that runs at the application layer and extends the existing WiFi-Direct protocol, which limits group owner negotiation to two devices only. Simulation results demonstrate the effectiveness of the proposed selection strategy in achieving near-optimum results.

Optimized group owner selection in WiFi direct networks

Failure recovery in wireless content distribution networks with device-to-device cooperation

With the current exponential growth in traffic and service demands, device-to-device (D2D) cooperation is identified as a major mechanism to enable 5G networks to effectively and efficiently augment network resources. The effectiveness of D2D cooperation depends on a wide range of decision making processes that include cluster formation, resource allocation, in addition to connection and mobility management. Irrespective of the D2D cooperation scenario whether in sensor, ad hoc, or cellular networks, the literature normally assumes that devices selected as relays or data sources are reliable; this means that they will maintain the connection till the communication session ends. Yet, this assumption is challenged in practice since devices’ batteries can be depleted (e.g., sensors in an IoT network) and devices can move leading to connection termination (e.g., mobile users in a WiFi network or cars in a vehicular ad hoc network). To this end, we address the problem of reliable D2D cooperation in wireless networks by proposing a novel approach that is proactive by utilizing reliability metrics in the decision making process, and scalable by having low implementation complexity suitable for dense networks. These differentiating factors are shown to enhance the overall network reliability compared to standard techniques and to facilitate dynamic operation which is essential for practical implementation. Performance is evaluated using extensive simulations in addition to test bed experimental demonstration in order to quantify gains and extract insights on a range of existing design tradeoffs.

pdf/a-proactive-scalable-approach-for-reliable-cluster-formation-1wbwarmqos.pdf

A proactive scalable approach for reliable cluster formation in wireless networks with D2D offloading

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An optimized UAV trajectory planning for localization in disaster scenarios

Optimized group owner selection in WiFi direct networks

Failure recovery in wireless content distribution networks with device-to-device cooperation

A proactive scalable approach for reliable cluster formation in wireless networks with D2D offloading